{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 机器学习策略1.0—基于逻辑回归的股市涨跌趋势预测(及SVM策略)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "import warnings; warnings.simplefilter('ignore') #忽略可能会出现的警告信息，警告并不是错误，可以忽略；"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 数据获取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2\n"
     ]
    }
   ],
   "source": [
    "hs300 = ts.get_k_data('hs300', start = '2016-01-01', end = '2019-08-18')   #训练集数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300.set_index('date', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300['returns'] = hs300['close'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300.dropna(inplace=True)            #有NaN值的话会影响机器学习算法, 会报错的，对NaN的处理： dropna, mean（）等；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>code</th>\n",
       "      <th>returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>3382.18</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>3518.22</td>\n",
       "      <td>3377.28</td>\n",
       "      <td>162116984.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.002412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>3482.41</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3543.74</td>\n",
       "      <td>3468.47</td>\n",
       "      <td>145966144.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.017544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>3481.15</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3481.15</td>\n",
       "      <td>3284.74</td>\n",
       "      <td>44102641.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.069334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>3371.87</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3418.85</td>\n",
       "      <td>3237.93</td>\n",
       "      <td>185959451.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.020392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-11</td>\n",
       "      <td>3303.12</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3342.48</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>174638387.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.050307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               open    close     high      low       volume   code   returns\n",
       "date                                                                        \n",
       "2016-01-05  3382.18  3478.78  3518.22  3377.28  162116984.0  hs300  0.002412\n",
       "2016-01-06  3482.41  3539.81  3543.74  3468.47  145966144.0  hs300  0.017544\n",
       "2016-01-07  3481.15  3294.38  3481.15  3284.74   44102641.0  hs300 -0.069334\n",
       "2016-01-08  3371.87  3361.56  3418.85  3237.93  185959451.0  hs300  0.020392\n",
       "2016-01-11  3303.12  3192.45  3342.48  3192.45  174638387.0  hs300 -0.050307"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs300.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. 数据处理——特征工程处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#特征工程不仅可以选择close(1)-(7)， 也可以选择SMA， SMA（5）， CCI等技术指标（TBD version 1.1, 1.2 ...）\n",
    "\n",
    "#特征的生成；\n",
    "for i in range(1, 8, 1):\n",
    "    hs300['close - ' + str(i) + 'd'] = hs300['close'].shift(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>close - 6d</th>\n",
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       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>3382.18</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>3518.22</td>\n",
       "      <td>3377.28</td>\n",
       "      <td>162116984.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.002412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>3482.41</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3543.74</td>\n",
       "      <td>3468.47</td>\n",
       "      <td>145966144.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>3481.15</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3481.15</td>\n",
       "      <td>3284.74</td>\n",
       "      <td>44102641.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.069334</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>3371.87</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3418.85</td>\n",
       "      <td>3237.93</td>\n",
       "      <td>185959451.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.020392</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-11</td>\n",
       "      <td>3303.12</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3342.48</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>174638387.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.050307</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-12</td>\n",
       "      <td>3214.82</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3242.25</td>\n",
       "      <td>3174.55</td>\n",
       "      <td>128225796.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.007286</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>3240.48</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3257.30</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>120666494.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.018606</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-14</td>\n",
       "      <td>3076.64</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3226.66</td>\n",
       "      <td>3072.04</td>\n",
       "      <td>134537671.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.020815</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3478.78</td>\n",
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      ],
      "text/plain": [
       "               open    close     high      low       volume   code   returns  \\\n",
       "date                                                                           \n",
       "2016-01-05  3382.18  3478.78  3518.22  3377.28  162116984.0  hs300  0.002412   \n",
       "2016-01-06  3482.41  3539.81  3543.74  3468.47  145966144.0  hs300  0.017544   \n",
       "2016-01-07  3481.15  3294.38  3481.15  3284.74   44102641.0  hs300 -0.069334   \n",
       "2016-01-08  3371.87  3361.56  3418.85  3237.93  185959451.0  hs300  0.020392   \n",
       "2016-01-11  3303.12  3192.45  3342.48  3192.45  174638387.0  hs300 -0.050307   \n",
       "2016-01-12  3214.82  3215.71  3242.25  3174.55  128225796.0  hs300  0.007286   \n",
       "2016-01-13  3240.48  3155.88  3257.30  3155.88  120666494.0  hs300 -0.018606   \n",
       "2016-01-14  3076.64  3221.57  3226.66  3072.04  134537671.0  hs300  0.020815   \n",
       "\n",
       "            close - 1d  close - 2d  close - 3d  close - 4d  close - 5d  \\\n",
       "date                                                                     \n",
       "2016-01-05         NaN         NaN         NaN         NaN         NaN   \n",
       "2016-01-06     3478.78         NaN         NaN         NaN         NaN   \n",
       "2016-01-07     3539.81     3478.78         NaN         NaN         NaN   \n",
       "2016-01-08     3294.38     3539.81     3478.78         NaN         NaN   \n",
       "2016-01-11     3361.56     3294.38     3539.81     3478.78         NaN   \n",
       "2016-01-12     3192.45     3361.56     3294.38     3539.81     3478.78   \n",
       "2016-01-13     3215.71     3192.45     3361.56     3294.38     3539.81   \n",
       "2016-01-14     3155.88     3215.71     3192.45     3361.56     3294.38   \n",
       "\n",
       "            close - 6d  close - 7d  \n",
       "date                                \n",
       "2016-01-05         NaN         NaN  \n",
       "2016-01-06         NaN         NaN  \n",
       "2016-01-07         NaN         NaN  \n",
       "2016-01-08         NaN         NaN  \n",
       "2016-01-11         NaN         NaN  \n",
       "2016-01-12         NaN         NaN  \n",
       "2016-01-13     3478.78         NaN  \n",
       "2016-01-14     3539.81     3478.78  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs300.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs_7d = hs300[[x for x in hs300.columns if 'close' in x]].iloc[7:]   #从第八行开始没有NaN地方开始取值作为features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2016-01-14</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
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       "      <td>3478.78</td>\n",
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       "      <td>2016-01-15</td>\n",
       "      <td>3118.73</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
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       "    <tr>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>3130.73</td>\n",
       "      <td>3118.73</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-19</td>\n",
       "      <td>3223.12</td>\n",
       "      <td>3130.73</td>\n",
       "      <td>3118.73</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-20</td>\n",
       "      <td>3174.38</td>\n",
       "      <td>3223.12</td>\n",
       "      <td>3130.73</td>\n",
       "      <td>3118.73</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-21</td>\n",
       "      <td>3081.35</td>\n",
       "      <td>3174.38</td>\n",
       "      <td>3223.12</td>\n",
       "      <td>3130.73</td>\n",
       "      <td>3118.73</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-22</td>\n",
       "      <td>3113.46</td>\n",
       "      <td>3081.35</td>\n",
       "      <td>3174.38</td>\n",
       "      <td>3223.12</td>\n",
       "      <td>3130.73</td>\n",
       "      <td>3118.73</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-25</td>\n",
       "      <td>3128.89</td>\n",
       "      <td>3113.46</td>\n",
       "      <td>3081.35</td>\n",
       "      <td>3174.38</td>\n",
       "      <td>3223.12</td>\n",
       "      <td>3130.73</td>\n",
       "      <td>3118.73</td>\n",
       "      <td>3221.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-26</td>\n",
       "      <td>2940.51</td>\n",
       "      <td>3128.89</td>\n",
       "      <td>3113.46</td>\n",
       "      <td>3081.35</td>\n",
       "      <td>3174.38</td>\n",
       "      <td>3223.12</td>\n",
       "      <td>3130.73</td>\n",
       "      <td>3118.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-27</td>\n",
       "      <td>2930.35</td>\n",
       "      <td>2940.51</td>\n",
       "      <td>3128.89</td>\n",
       "      <td>3113.46</td>\n",
       "      <td>3081.35</td>\n",
       "      <td>3174.38</td>\n",
       "      <td>3223.12</td>\n",
       "      <td>3130.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              close  close - 1d  close - 2d  close - 3d  close - 4d  \\\n",
       "date                                                                  \n",
       "2016-01-14  3221.57     3155.88     3215.71     3192.45     3361.56   \n",
       "2016-01-15  3118.73     3221.57     3155.88     3215.71     3192.45   \n",
       "2016-01-18  3130.73     3118.73     3221.57     3155.88     3215.71   \n",
       "2016-01-19  3223.12     3130.73     3118.73     3221.57     3155.88   \n",
       "2016-01-20  3174.38     3223.12     3130.73     3118.73     3221.57   \n",
       "2016-01-21  3081.35     3174.38     3223.12     3130.73     3118.73   \n",
       "2016-01-22  3113.46     3081.35     3174.38     3223.12     3130.73   \n",
       "2016-01-25  3128.89     3113.46     3081.35     3174.38     3223.12   \n",
       "2016-01-26  2940.51     3128.89     3113.46     3081.35     3174.38   \n",
       "2016-01-27  2930.35     2940.51     3128.89     3113.46     3081.35   \n",
       "\n",
       "            close - 5d  close - 6d  close - 7d  \n",
       "date                                            \n",
       "2016-01-14     3294.38     3539.81     3478.78  \n",
       "2016-01-15     3361.56     3294.38     3539.81  \n",
       "2016-01-18     3192.45     3361.56     3294.38  \n",
       "2016-01-19     3215.71     3192.45     3361.56  \n",
       "2016-01-20     3155.88     3215.71     3192.45  \n",
       "2016-01-21     3221.57     3155.88     3215.71  \n",
       "2016-01-22     3118.73     3221.57     3155.88  \n",
       "2016-01-25     3130.73     3118.73     3221.57  \n",
       "2016-01-26     3223.12     3130.73     3118.73  \n",
       "2016-01-27     3174.38     3223.12     3130.73  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs_7d.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.92956478, -1.1239447 , -0.9427184 , ..., -0.70256927,\n",
       "         0.0322656 , -0.14975484],\n",
       "       [-1.23782695, -0.92714925, -1.12189169, ..., -0.5015157 ,\n",
       "        -0.70228784,  0.03291939],\n",
       "       [-1.20185704, -1.23523943, -0.92516942, ..., -1.00762122,\n",
       "        -0.50122317, -0.70169862],\n",
       "       ...,\n",
       "       [ 0.45176988,  0.40353428,  0.50489278, ...,  0.27621265,\n",
       "         0.32114267,  0.43963371],\n",
       "       [ 0.4865408 ,  0.45341469,  0.40501932, ...,  0.41944611,\n",
       "         0.2765481 ,  0.32182185],\n",
       "       [ 0.53611933,  0.48816621,  0.45488118, ...,  0.31242504,\n",
       "         0.41978946,  0.27722335]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = hs_7d\n",
    "X_train = sklearn.preprocessing.scale(X_train)    #对features进行标准化；\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2019-08-05   -1.0\n",
       "2019-08-06   -1.0\n",
       "2019-08-07    1.0\n",
       "2019-08-08   -1.0\n",
       "2019-08-09    1.0\n",
       "2019-08-12   -1.0\n",
       "2019-08-13    1.0\n",
       "2019-08-14    1.0\n",
       "2019-08-15    1.0\n",
       "2019-08-16    0.0\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算出训练集的labels；\n",
    "y_train = np.sign(hs_7d['close'].pct_change().shift(-1))       #非常重要；拿到下一天的收益，用.shift(-1)\n",
    "y_train.replace(to_replace= np.NaN, value = 0,inplace = True)\n",
    "y_train[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'reshape'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-4b2f98dbd481>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5177\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5178\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5179\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5181\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'reshape'"
     ]
    }
   ],
   "source": [
    "#y_train = y_train.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [ 0.]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(y_train).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.],\n",
       "       [-1.],\n",
       "       [ 1.],\n",
       "       [-1.],\n",
       "       [ 1.],\n",
       "       [-1.],\n",
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       "       [ 1.],\n",
       "       [ 0.]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = np.asarray(y_train).reshape(-1,1)\n",
    "y_train[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 3. 逻辑回归预测股价趋势算法实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = linear_model.LogisticRegression(C=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1000, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5490867579908676"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300['prediction'] = np.NaN\n",
    "hs300['prediction'].ix[7:] = lm.predict(X_train)    #返回的是我预测的训练集的labels；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>open</th>\n",
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       "      <th>high</th>\n",
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       "      <th>close - 2d</th>\n",
       "      <th>close - 3d</th>\n",
       "      <th>close - 4d</th>\n",
       "      <th>close - 5d</th>\n",
       "      <th>close - 6d</th>\n",
       "      <th>close - 7d</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>3382.18</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>3518.22</td>\n",
       "      <td>3377.28</td>\n",
       "      <td>162116984.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.002412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2016-01-06</td>\n",
       "      <td>3482.41</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3543.74</td>\n",
       "      <td>3468.47</td>\n",
       "      <td>145966144.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>3481.15</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3481.15</td>\n",
       "      <td>3284.74</td>\n",
       "      <td>44102641.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.069334</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>3371.87</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3418.85</td>\n",
       "      <td>3237.93</td>\n",
       "      <td>185959451.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.020392</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-11</td>\n",
       "      <td>3303.12</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3342.48</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>174638387.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.050307</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-12</td>\n",
       "      <td>3214.82</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3242.25</td>\n",
       "      <td>3174.55</td>\n",
       "      <td>128225796.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.007286</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>3240.48</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3257.30</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>120666494.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.018606</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-14</td>\n",
       "      <td>3076.64</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3226.66</td>\n",
       "      <td>3072.04</td>\n",
       "      <td>134537671.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.020815</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>3200.89</td>\n",
       "      <td>3118.73</td>\n",
       "      <td>3216.80</td>\n",
       "      <td>3101.05</td>\n",
       "      <td>124601779.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.031922</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>3068.23</td>\n",
       "      <td>3130.73</td>\n",
       "      <td>3165.62</td>\n",
       "      <td>3066.73</td>\n",
       "      <td>101128386.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.003848</td>\n",
       "      <td>3118.73</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               open    close     high      low       volume   code   returns  \\\n",
       "date                                                                           \n",
       "2016-01-05  3382.18  3478.78  3518.22  3377.28  162116984.0  hs300  0.002412   \n",
       "2016-01-06  3482.41  3539.81  3543.74  3468.47  145966144.0  hs300  0.017544   \n",
       "2016-01-07  3481.15  3294.38  3481.15  3284.74   44102641.0  hs300 -0.069334   \n",
       "2016-01-08  3371.87  3361.56  3418.85  3237.93  185959451.0  hs300  0.020392   \n",
       "2016-01-11  3303.12  3192.45  3342.48  3192.45  174638387.0  hs300 -0.050307   \n",
       "2016-01-12  3214.82  3215.71  3242.25  3174.55  128225796.0  hs300  0.007286   \n",
       "2016-01-13  3240.48  3155.88  3257.30  3155.88  120666494.0  hs300 -0.018606   \n",
       "2016-01-14  3076.64  3221.57  3226.66  3072.04  134537671.0  hs300  0.020815   \n",
       "2016-01-15  3200.89  3118.73  3216.80  3101.05  124601779.0  hs300 -0.031922   \n",
       "2016-01-18  3068.23  3130.73  3165.62  3066.73  101128386.0  hs300  0.003848   \n",
       "\n",
       "            close - 1d  close - 2d  close - 3d  close - 4d  close - 5d  \\\n",
       "date                                                                     \n",
       "2016-01-05         NaN         NaN         NaN         NaN         NaN   \n",
       "2016-01-06     3478.78         NaN         NaN         NaN         NaN   \n",
       "2016-01-07     3539.81     3478.78         NaN         NaN         NaN   \n",
       "2016-01-08     3294.38     3539.81     3478.78         NaN         NaN   \n",
       "2016-01-11     3361.56     3294.38     3539.81     3478.78         NaN   \n",
       "2016-01-12     3192.45     3361.56     3294.38     3539.81     3478.78   \n",
       "2016-01-13     3215.71     3192.45     3361.56     3294.38     3539.81   \n",
       "2016-01-14     3155.88     3215.71     3192.45     3361.56     3294.38   \n",
       "2016-01-15     3221.57     3155.88     3215.71     3192.45     3361.56   \n",
       "2016-01-18     3118.73     3221.57     3155.88     3215.71     3192.45   \n",
       "\n",
       "            close - 6d  close - 7d  prediction  \n",
       "date                                            \n",
       "2016-01-05         NaN         NaN         NaN  \n",
       "2016-01-06         NaN         NaN         NaN  \n",
       "2016-01-07         NaN         NaN         NaN  \n",
       "2016-01-08         NaN         NaN         NaN  \n",
       "2016-01-11         NaN         NaN         NaN  \n",
       "2016-01-12         NaN         NaN         NaN  \n",
       "2016-01-13     3478.78         NaN         NaN  \n",
       "2016-01-14     3539.81     3478.78         1.0  \n",
       "2016-01-15     3294.38     3539.81         1.0  \n",
       "2016-01-18     3361.56     3294.38         1.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs300.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1.0    545\n",
       "-1.0    331\n",
       "Name: prediction, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs300['prediction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300['strategy'] = (hs300['prediction'].shift(1) * hs300['returns'] + 1).cumprod()  # 今天收盘后计算出prediction，shift(1)从而运用在明天的return上\n",
    "hs300['cum_ret'] = (hs300['returns']+1).cumprod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122cf6f90>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vlwUoZImIiEgfVmV1EBbox2lj4hkWF+o+Zqv3clVuClkiIiLSa63OLuP+D7Zgmmar5937FPoTExrAol/OZFRCGPNnpR3lKluntwtFRESk15r35HcAnDN+IIVVNk4bE9/ifPPNoIMsviy56YSjXuPhKGSJiIhIr1RU1bRdznmPfwvArj+dgV+zRe2VVgeRwZajXlt7aLpQREREeh2H08UX2w4ccnxPcQ3gDld55XWU1tiJCPZ+T6zWaCRLREREep1/fL6Tv3+x65DjW/MrGREfxg0vr+GbXcUAXDhx0NEur100kiUiIiK9zqa8ylaPf7LlAC6XyfdZpZ5jydG9o2XDwTSSJSIiIr1OYisd2ycNieDDDflMHByBvd7lOZ4SE3w0S2s3jWSJiIhIr1NndwIQFuBHRLA/Qf6+PH/VFADu/3Bri2uH9JLmowfTSJaIiIj0OmW1dgZFBPHhjTNxukyCLL4EW/yICrFQWmPnhpOGMm9yEp9uOUBMaO98u1AhS0RERHqd8joHabEhRBzUnuHN66dTXutgcnIkAENPDPVGee2ikCUiIiK9QkWtg4o6B0OigymvdTAoIuiQa4bG9t5QdTCFLBEREekVzn/iW/YW1/B/J6axt7iGmcNivF1Sl2jhu4iIiPQKexsajT69dA8ABZVWb5bTZQpZIiIi0iudftA+hX2NQpaIiIj0Oj8+bggXZw72dhldopAlIiIivYK/r+H5HBMa4MVKuodCloiIiPQKoQFN7+NFh/TO3lcdoZAlIiIivU50L20w2hEKWSIiItIr1Nicns+RwQpZIiIiIl1mq3did7pICHdvDN3aBtF9jZqRioiIiNc1jmJdf2IaZ48fqIXvIiIiIt2hxlYPQEiAX78IWKCQJSIiIr1AldUdspq/YdjXdfpPYhjGUOB+YA2QBJSYpvmHg665CrgeaOyL/5xpmi919jtFRESkf6puGMkKDVTIAogCXjdN830AwzC2GIbxoWmaqw+67lLTNLO68D0iIiLSj63OLuP2tzcA/aMJaaNOhyzTNH846JAPUNPKpb80DKMACAb+aZpm6cEXGIYxH5gPMGTIkM6WJCIiIn3QvCe/83xufLuwP+iWNVmGYVwAfGya5raDTi0FHjJN8xFgFfBma/ebpvmMaZqZpmlmxsbGdkdJIiIi0gdFBPt7u4Ru0+WJT8MwZgOzgZsOPmea5t5mP34B/M8wDF/TNJ0HXysiIiLHnjp7y0hgGMZhrux7ujSSZRjGWcAc4NdAgmEY0w3DiDIMI7zh/IOGYTQGueHAXgUsERERaZRbVuvtEnpMV94unAy8gXsa8EsgBHgcuAAoBRYCBcCThmHsBTKAK7tasIiIiPQfdY7+O/bSlYXvq4HQNq55rLPPFxERkf7P6nABcMW0IVw6pX+9/KZmpCIiIuI11oaRrPMnDGLsoAFerqZ7KWSJiIiI1zSGrEB/Xy9X0v0UskRERMRrbPXu6cJA//4XSfrfn0hERET6jMaRrAA/jWSJiIiIdBtrw0hWgEayRERERLqm0urg7dW5mKaJrR+vyeo/W12LiIiI19nqnVh8fVp0brfXu7D4+Xg+n/7o1xRUWslIGtC08F3ThSIiIiKte2t1LiPvXsLfP9/lOfb7RZuZ+sBn7Ct1d3b/22c7KKi0AlBYacPqcOFjgL9v/9lOp5FCloiIiHSLlXtKAFidUwZAYaWVf3+bRXmtg8e/dAev7QVVnjcJi6ttWB1OAv19+9WehY0UskRERKRblNXaAdhTVA1AfoXVcy6nYSSruNrGqIRwz2dbvatfrscChSwRERHpJqU17pC1v7wOq8NJUZUNgNiwAMpqHQAUV9tJiwnB4utDUeNIll//jCP9808lIiIiR11jkDJN2FNUQ1G1O2QNjwulotaOaZoUV9uICQsgOtRCcZUdq0ayREREpK+qsdUfle8pq7UzY2g0AJvzKjwjWcPiQimvc1Bjd2KrdxETaiEmNMCzJitAIUtERET6mpeWZ5F+38fkldf16PfUO11U1DmYnBxJiMWXr3cWU1BpJSLYn7iwAGrtTk8N0SEBxIUFcKDS6g5Z/XS6UH2yRERE+qk1OWXc8/5mAF5cnk1ogC+/mD2sR97kq6hzYJoQExpAamwIi9bnAe6pwohgCwC7Ct0L4mPCAkiJCeHb3cWEBvj1y30LQSNZIiIi/dZfP93h+fzU0t088skOdhfV9Mh3Nb5ZGBHsz29OG+k5njFoABHB/gCs31cOwKCIINJiQ7A6XOw4UEWIpX+O+ShkiYiI9EOVVgc7D1Qze2QsV81I8Rxf3tDLCsDpMj3Bp6sq6tyL3gcE+TN7VBxf/vYkfnzcEO47J53IhpGsd9fux+LnQ0p0MGkxoQ111nPp1CHdUkNvo5AlIiLSD41b8AkFlVYmDI5kwbnp7H3wTBIHBHoahgL85ZPtnPf4t2zNr+zy91VZ3YvrwwLdo1apMSE8cEEGA4L9iQ0LAKCwykZ4oD9+vj4MiwvFMOD8CQM5bUx8l7+/N1LIEhER6Weav02YFBkEgGEYjE+KYNP+Cs+5FQ2Bq3EUqiuqbY0h69Cpv+FxofzhvHQABjXUExsWwFvXz2DhvHFd/u7eqn9OgoqIiBzD8iua3iTMTIn0fM5IGsCSzQVUWh2EB/pTa3dvzmyrd3X5O6sbRrJCAw6NFoZh8JPpKYxKCPeELIDJyZGHXNufaCRLRESkn8krd29n88b8aSRHh3iOpw90b2ezeb97erBx9Kk7R7JCWxnJajQ1NYpBEUGHPd/fKGSJiIj0M40jWQMPCjQj4sMA2F1UjWmanpGsyjoHtnonv3t3I4WVVjqjcU1Wf31TsDMUskRERHqhkmob819c5dkPsCPyyq0YBiQMCGxxPCE8kAA/H7KKa/jrpzs8z660Oli8sYBXV+bw54+3d6reals9IRZffH26vwdXX6WQJSIi0gs9981ePtlygFdWZHf43pV7S0iNCcHft+Vf8z4+BinRIWSV1LB0R5HneGVdPeUNfa4COtkYtNpaf8SpwmORQpaIiEgvVO8yATA7eN/PX17Nij2lXDhxUKvnU2KC2VtcQ63dyZz0eGJCLVRaHYe0YOioalt9q4vej2UKWSIiIr1QRa17MXpHpwuX7SwG4Ippya2eT4kOYV9pHQUVVhLCAwkP8qeizkGV7fBvB7ZHla2e0E4GtP5KIUtERKQXsDqc3PrmerYXVAGQXere/ia3rP0bO9fa66m21XPb3JGe/QIPNjAiCLvTRZWtnvgBgVTW1fPhhnw+3XIAgHpnR8fO3KqtDsI0ktWCQpaIiIiXrc0pY9Q9S3hzdS7PLttDta2ebQ1ha0NuOUs2FfD+uv1tPqegwv1mYEJ44GGvSWy2GD4hPJApDX209ha7Q5213tnh+k3TpKjapunCgyhkiYiIeNnanKb9A0MC/Hjyq11U1Dm4bOoQCqtsXP/yan79+jqcriOPMnlC1oDDh6zmbR0SwgN55OLx/OsnmZ5jVsehIauw0kpxtQ2AnJJavmmYkgR4b+1+bnx9HftK6zh5VFwbf9Jji0KWiIiIl1Va3euvYkIt5JTW8vKKHOamJ/DghRmMSgjzXLex2ZY4rSlo6HGVOODwDT+bh6yMpAGEBPhx2ph4nr5yMgBWx6Hd36c+8DmZ938GwAl//pIrnlvpOXfTG+tYtD6PsAA/LpzU+mL7Y5VCloiIiJeV1dgZEORPWmwoX2wrpKLOwY+mDAYgKqRpbVXzzZ2bM02Td9bk8nJDu4cjTRdGBjctTm/+JuGc9ASSIoOwtTKS1ejBj7Z6Ptc7W4axlJgQ/HwVK5rT5KmIiIiXldY6iAqxEBcW4DmWMWgAAOOSIvhutztcNS6KP9iSTQXc8t/1AAyLCyXI4nvY7zIMgwcvzPB0f28uwM/niGuynv56j+dzRZ2DyGaL69WD9FAKWSIiIl5WVmMnMtifuDD3CFRcWAAxoe7AdctpI5iaGsl/vsvmnbX7uXpmKmMbAphpmuSW1bFsV9MaqeYjVYdz2dQhrR4P9Pdtdbqw1ZprHRhGU7I6a1xiu+47lmhcT0RExMtKa+xEhVgYHOVeL9W4kTOAxc+Hk0fFk9gwBXje4996zn20sYBZD3/JqytzGBEfio8BPz9paKfrcIesliNZBy+2b3yDsLzW7lkMf8/ZY7huVlqnv7e/UsgSERHxEtM0Wb+vnLJaO5HBFi6bOoT//GwKD180/pBrz2/o4O50meSU1AKQU1rrOT8nPYE9D57FyaPiO11PoL/PISGruqFJKUCIxZdXrzsOcI9kFVW5Q1b6wPAWo1ripulCERERL3lzVS63vb0BcC9wD/T35aSRrbdBmD40mmW3zWbWw1/y3Dd7KK62M6BhanBobAhXTm+9w3tHBPr5UlbjaHGsecj67ZyRnnVYizflU1Lt7kbfOLUpLSlkiYiIeMmmvKaWDIMiD992odHgqGBOHBHLC8ubNo2OCrHw2S0ndstIUqC/L1vyK3lw8VZuPX0kfr4+VDW0l3hoXgaXTBni+fmdNe7mqMelRnmmOaUlhSwREREvadyUGfAsZm/LL2YPY+mOIs/PcWEB3TdV1/CYp5fuweZwseDcdKobamzsvdW8q/u6e0877PY9ojVZIiIiR11BhZVxCz7m3bVNW+WMTgg/wh1NpqZGcfvcUZ6fY8O6b6out2GNl7+vwTtrcgGaNo4OdIcrwzB4aF4GH904SwGrDQpZIiIiR9n2A1VUNhvFSh8YfsTeVgf7+UlDOW2Me4F7WGD3TUrtadi/8Nzxg6i01mN1OD0jWc03f75kyhDGDGxfKDyWKWSJiIgcZQcatr9ZdttsshaexYc3zurwMy6anARA+sD2TTO2xyWZ7i7zU1Pdm0YXVto8U5qh3RjmjhX6jYmIiBxlhQ0hqytTfXPSE1h372kEW7rvr/K7zhrNbXNHsbxh+57CKiu7CqsJ8PPRG4SdoJAlIiJylB2otBER7E+gf/unCFvT3WuiDMPA4md4tvc5UGlj7b4yxiUNwF/7EnaYfmMiIiJH0SebC3hpRTYRQW1vf+Mt8Q3d5feV1bJ5fyWThkR6uaK+SSFLRETkKKmodTD/pdUAZJXUtnG190QG+2Px8+GDDXnYnS4yU6K8XVKfpOlCERGRo6RxG5xRCWFd2mOwpxmGwYj4UDbtr8TXx2BamkJWZ2gkS0RE5CgprHIveH/wwgzOmzDIy9UcWWPfrsnJkYQF9t6pzd5MIUtEROQoWL+vnGteWAU0rXnqzWIaFr/PSU/wciV9l6YLRUREjoLzHv/W87kvtEO4ZmYqAJcfN8TLlfRdGskSERHpQd/tLibljg9bHLP49f6/fmNCA7h97qgut5k4lvX+f8oiIiJ92Bs/7PN2CeIlmi4UERHpQUVVNs/nq2akEB2iTZWPFQpZIiIiPcTlMlm/r9zz84Jz071YjRxtClkiIiI9ZF9ZLTV2J3efNZrzJ/bulg3S/bQmS0REpIdsza8CYEpKVJ94o1C6l0KWiIhID9lWUIlhwIj4MG+XIl6gkCUiItJDtuVXkRodQpBFbRCORQpZIiIiPWRbQSWjEjWKdaxSyBIREekBNbZ6sktrGdWwB6Acezr9dqFhGEOB+4E1QBJQYprmHw66JhB4BNgPDAcWmqa5o/PlioiI9A3vr8vDNGFMokLWsaorLRyigNdN03wfwDCMLYZhfGia5upm19wE5Jim+bBhGBnAc8CsLnyniIhIr1ZUZcPhdPHg4q1MT4vmpJGx3i5JvKTTIcs0zR8OOuQD1Bx07Czgdw3XbzQMY7xhGOGmaVZ29ntFRER6s6kPfIZpuj//6uRh+PlqZc6xqlv+yRuGcQHwsWma2w46FQdUNfu5suHYwffPNwxjlWEYq4qKirqjJBEREa9oDFhhAX5MS4v2bjHiVV0OWYZhzAZmAze3croQaP5aRXjDsRZM03zGNM1M0zQzY2M1rCoiIr1PUZWNh5dsw+F0tev608bE4+Nj9HBV0pt1KWQZhnEWMAf4NZBgGMZ0wzCiDMNoXOX3ITC94doMYL2mCkVEpC968KOtPPHVbr7ecfgZF9M08fUxSI4O5o/njz2K1Ulv1JW3CycDbwCrgC+BEOBx4AKgFFgIPAY8YhjG3XrnuiUAACAASURBVMAw4JquFiwiIuIN5XUOAPIrrIe9ptpWj9NlcvlxQwgJ0PbAx7quLHxfDYS2cU0d8IvOfoeIiEhvUW2rB2DngSrqnS4e+3wnPzs+lagQi+eaioYgFhFkafUZcmzRKw8iIiLtsLuwGoDNeZW8vCKbf3yxiz99uLXFNeW17pAVHuR/1OuT3kdjmSIiIm2od7ooqbEDsCq7jFXZZQDYmy2Ct9e7OPsf3wAQEayQJRrJEhERaVONzQnAnPT4FsctzXpg7Sxs6likkCWgkCUiItKmKpt7GvDEEXGkD2zaJsfi1/TX6PaCppAVGaw1WaKQJSIi0qbGRe8Rwf48dcVkz3GjWRusxpD12KUTiA8PPKr1Se+kkCUiItKGmoaQFRLgR2xYgOd4tbXe83lzXiVjEsM5b8Kgo16f9E4KWSIiIm2oaghToQF+BPr7eo43jnDVO12szSljcnKkV+qT3kkhS0REpA2NC99DD2ow2jiSta2gihq7k8wUhSxpopAlIiLShuqGhe+hge6QteiXM0mNCaHS6j6+q6GHVvrAAd4pUHolhSwREZE2VB80kpWRNICJgyM804XF1TYAYkMDWn+AHJMUskRERNrQOC0YYmlajxUW6OdZq1VaY8fPxyA8SD2+pYlCloiIyBGszi7j480FBPr74Nes+WhYoD9VVgcul0lJtZ2oEAtG854OcsxT5BYRETmCa1/4gbKGPQmbiwm14DKhrNZOSY2daE0VykE0kiUiIkdUZXVQZT00ZBwLTNP0BKyfnzS0xbnYMHfD0cIqGyU1NmJC1eVdWlLIEhGRwzJNk7P+/g2n//VrXC7T2+V0mGl2rWarw70B9O1zR3H73FEtzsWFu0euiqpsnulCkeY0XSgiIof16vc55JTWAvDVjkJOHhXfxh29x7UvrOKHrFKSo4M5b8IgrpmZ2q77TNPk5RXZnJGRiLMhWLa2oL3xTcL8ijqKqmxEh2i6UFrSSJaIiLSqtMbOPe9tImPQACx+Pny6pdDbJXXIZ1sPUFHnYENuBR9vKmj3fbllddzz/mau/s8PnmnSsED/Q65r3F7n1ZU51DmczBwe3T2FS7+hkCUiIq1am1OGy4S7zxrNSSNi+XpHUZen346Wxv5VANEhFk/T0PYoqbEDsCG3goo693PCAw8dyQoJ8CPE4sv63ApSY0I4cURcF6uW/kYhS0REWrUmpww/H4NxSRGcmZHI/vI6XlmZ4+2y2iWruAaAJy6fxJkZiRRUWtt9b3GVzfN5T5G7k3t40KEjWQBnjxvIhZMG8db10/H1UfsGaUlrskRExCOruIb/rtpHlbWerJIaRsSHEWTx5bwJA3lpRTbPf7uXqalRRIVYDtksuTfJKnGHrJToEPYW11Be68DqcBLo78uSTfn8/JU1rLv3dAYcFJ52FVaTW1br+fnWtzYArY9kATx00bge+hNIf6CQJSIiHle/8AN7imo8P184cRAAhmEwb1ISv3t3I6f/9WsARsSH8snNJ3qlzrZsyavEz8cgNSaELfnuVgtfbCvkzIxE7v9wK6YJuwqrmJwc5bnH6nBy6qNLW31eeCtrskTaoulCEZF+JKu4hvJae6futde7yCquYfbIWM+x4fFhns9zxya0uH7HgeojrnUyTdOzhmv57hJu+e867PWuTtXWUauzy0gfGE6QxZeEcHfIuuGVNew4UEV5Q9+r3LI6Kq3uEa5/fb2Hv32203N/aIAfV81I8fx8uOlCkSNRyBIR6Sf+/e1eTnrkKy7710oczo6Hmf3ldbhM9zqj5OhgAIbGhnjON+8D9auThwGwaX9Fq89anV3KqHuWcMMrawD4z3d7eWfNfv61bE+H6+ooh9PF+txyzyjVcWlRXNvQvuHhJds8i+JzSmq54tmVjLpnCX/6aCtPLd3teUZ0qIUF56YTEewOVwF++utSOk7/rRER6Qcq6hz8+ePtAGzNr+SjjfkdfkbjOqbk6GBevuY4zh0/kOlDW7Yl+OjGWSy8MMMzyrN+X+sha+mOYmz1LhZvKsDhdBES4F6d8smWA57eUz1lV2E1VoeL8YMHAODv68PdZ4/h9DHxfLa1EEtDYNp2oIoNua3XH9fQnuGTm0/g9fnTtCehdIpClohIH1NR5+AXr6xpsUD7213F1NqdvDF/GhHB/izbWdzh5+4udL9JlxwdwuCoYP5+2cRD+kONGRjOpVOHEB0awOjEcB5aso0F/9t8yLOaj3Bty6+irKEtwvp95Zzw8Jcdrq2R1eHk39/u5f11+1ttJ1Ftq+fu9za5a00Mb3FuXJI7dF1xXDJTU6IOG0SfumIST1+ZCUBcWCDT0tT/SjpHC99FRPqYT7cc4MON+dQ5nDx/1RS+21XMe2v3Exbgx6TkSGYMjea7XR0LWbX2ep5aupsxieHt3oPvx1MHc8/7m/nPd1mcPCqOijoHj3+5Cz9fg037K5mWFsWKPaWszi6ltNkGy/vL3R3Sgy2+LN9dwqlj2tdF3jRNfvnqWj7begBwh6jma8YA3l2Ty+rsMgBSY0JanLv8uGSqbU5+dfIw/vHFLr7PKnXfc8MMDMPgvbX72XGgirljE9tVj0hbNJIlItLHbM5zjxKtySnjxeVZ/PjZlXyy5QCnjYnH39eH0Qnh5FVYsdU72/3M7QVVFFfbufGU4e2eGrtiWjIf33QCMaEWXv8hh1+9tpZtBVVs2l8JwG9PH8mQqGC+2VVCWY2ds8clcvHkJMA90vW7dzdy7YurPL2o2vL93lI+23qAuenuBfh7imsOuaZxUfvk5Ej8fFv+FRcZYuGOM0YREuDHcWlNbxVOGBzBhMERLDg3nVevm9auWkTaQyFLRKSP2JJXydNLd/Pvb7MAd6C49/2mqbqfnzQUaNrupbi67bcMHU4X177wA/9dlQtAWmxIG3c0MQyDkQlhnJWRyEcbW25bs/3+uWSmRDFreAzLdxeTV15HbFgA954zBsOA9bnlbM13h7HyupZvKNba61m2s4hrX/ihRef273aX4GPA3WePBiC75NCQlVdhJTLYnzf/b/oRa5+WGs2po+M8o1giPUHThSIifYBpmpz592Wen/96yXhufmM9APHhASy9dbanMWhjyCqqsjEoIuiIz92SV8lnW5v2JBwcGdzh2iYOieSF5dkARAb7c+Mpwwnwc9dywcRBni7xUcEWwgL9GTdoAF9uK6S+YQF8SbMwuK+0lt8v2uKZEnx7dS4/nZHC2pwyHvt8J6MSwkiKDCYi2J/skloOll9Rx6DIIHza6L4eZPHl2Z9O6fCfVaQjFLJERPqAXYVNU2rnTxjIBROT+HJbEcelRXHehEEtOq97RrKabQ9zOGtyyjyf48ICCLJ0vIN7UmRTkFv0q5kkNQtqmSlRnDIqjs+3FdK4TP2MjEQWLt6GpWE6r6ihzs+3HuCaF1a1ePbfP9/JCSNiebEhxJ3WsH4rOTqk9ZBVbmVIdMeDokhPUMgSEenlXlqRzcNLtgHwfyemMX9WGgB/v2xiq9d7RrKqm0LW2pwyHv9yN9OHRnPZ1MEEW9z/+v9qe5HnmsFRnQsnze9LHHDoyNmCc9PJKa1l9kj3BsqZyZEA2Bt6eRU31Nl8yvHSKYM5ZXQ81724itmPfEVKdDCzhsdwy2kjAEiOCm4REMH95mFuWS3Tmq23EvEmhSwRkV4su6SGexpaEkQG+3PnGaPbvCc6xB2ylu0sIi4sgFNGx7Nw8TZWNiwcDw/04+LMwazOLmPpjiLOHT+QOofT02C0o2JDAzyfW9skeXBUMJ/e0rT9zpCDwlzjSFZhlbXFPaeOjuOMsQks3lRAVkktp6cneNZPpUQH88GGPOz1Lk/fq6eX7qHG7uSMDL0dKL2DFr6LiPRiK/eUej63t4Wnxc+HqBALH20s4JoXVvHHD7awcm8pN506HGh6K++HhhYGfzgvnX/9JJNxSRGdqrGt9U8Haxxpa/TSimwKq6wUVjaNvEUE+2MYBr86ebjn2Mhm7RqSo0NwmXh6hW3MreDJpbs4KyNRfa2k11DIEhHpxfY1azg6eUhku++bMLgpMD33zV7OHT+QX508nLSYELIaQtb2gioSwgOJCG5fX6wjee8Xx/PJzSe069rmb/NNbpg6fGfNfg5UWQlt6Aw/PM4dqFJimka9pqY2TQM2Hm9cl3Xj62uJDLZw11ltj/SJHC0KWSIivdi+0loGRQTx+vxpPHrJhHbfd8cZo1r8/NilE/D1MRgSHcziTQWszi5jW0EVoxLDDvOEjpkwOIIR8e1/VkzDFONb109ncFQQCxdvo7zWwfUnpvH1rbM9gapx7Ri0XPuVHO1uNZFVUsOG3HL2Ftdw8eQkBrbxNqXI0aQ1WSIi3azO7uSPH25hZHwYP23Y46+z9pXVMTgqqMNTYCPiw1h222xmNWxh0zh61Lh+6levrqG42s4JI2K6VF9nLf71LKwO5yE9qoItfoe8HfjYpROIPGi0LTrEQmiAH4s3FvD7RVsASFTAkl5GI1kiIt3s7vc28erKHO7732b2l9d1+jmmaZJdUtup3lWAZ1Tnp9OTPcduPGU48eEB5FVYsTtdjEronpGsjooNC/CMTP329JGe442bOjd33oRBnDAitsUxwzAYEhXs2RoHIHFAYA9VK9I5ClkiIt1obU4Zb6/J5cKJg/D1MXipob9TZ2zIraC42kZmSvvXYjXn62Ow7Y9zue+cdM+xwVHB/P3SptYPI+PDW7v1qDpvwiCyFp7F9vvnMjm5/e0Xmq/XAjRVKL2OpgtFRLrRs9/sJSLYnz+eP5ZqWz1v/JBDja2et9fksuCcdCYlR/Lx5gJuOGmoZ6qs2lbPRxvzmTA4gsJKGzOHu6fwPtiQh8XXp0sbFjdvUtpoSkpTkBka1/5tdHpaY5f49mpcl9VII1nS2yhkiYh0E9M0WbmnlJNHxRES4MeNpwzn7H98w0sr3KNZ//hyJ/tK3dOHJ4+KY3SiexTpljfW8cmWA57n/HDXqcSGBbBiTymTkiMYEOTfrXX6+Bg8dcVkNu2v6HCw6U1GxId6Pp+ZkUBYYPf+nkS6SiFLRKSbFFRaKa62Mb6h39TYQQN46ZqprM4uI8jflwcXb/Ncu2JPCaMTw6moc7QIWAAFFVaCLL5szqvgF7M71yC0LXPHJjB3bEKPPPtomTi4aRr1icsne7ESkdYpZImIdJPGjZbHJTUt3p41PJZZw2Ox17vIr7AS4O/DRxvzWb67hJ8dn8ry3cUA/PqU4dQ5nDzz9R4Kq6xU1Dlwme69/6R1ydqjUHo5hSwRkW5QWGXljx9sYUpKJBmDDn1DzuLnw4Jz3QvQy2rsfLz5AC6XyfLdJYRYfPnlycMorLI1hCwb+RVWfAyYNKRzXdiPBYZh8NdLxnf7dKpId9HbhSIi3eCN7/dhr3fx0Lxx+Pke+V+t04dGU1Hn4MvthWwrqGJ0Yjj+vj6eHlaFlTZWZZUyOjFc64zacMHEJE4eFe/tMkRapZAlItINlu0qZvzgCNJiQ9u89oThscSEWrjxtbWs3FvKsDj3PRY/HyKD/SmssrKnqMazMF5E+iaFLBGRLjBNE5fLZGteJRmD2heKokMDeH3+dGrsTgBPyAKICwuksMpGeZ2dqJCu7ykoIt6jNVkiIp20t7iGuX/7Glu9C4D0gYeuxTqcYXGhXDZ1MK99v4+RzbquJ0YEsre4BqvDpbVGIn2cQpaISCdYHU7+9OEWT8ACmNLBzux/Oj+D8ycM8myGDJAUGcRX24sAiAhWyBLpyxSyREQ64amlu/lsayETBkfw4jVT8fMxCLZ07F+pPj4Gxx208fOgiKa2BBFBmi4U6csUskREmnnt+xx+2FvKmRmJnDrm8G+tfberBIBHLh5PeDe+AZgU2bT/XqRGskT6NIUsEZFm/vnFLvaX1/HO2v0svfUklu0sJi02hBlDYzzXPLxkG99nlXLdrNQWi9a7Q/OQNUAhS6RP09uFIiINSmvs7C+v47wJAwF34Lr7vU38+F8rsTqcOF0mv/nvep74ajcA544f1O01DG0W2iKCNV0o0pcpZImINLjwiW8BuPy4ZGJCA3hzda7n3Jy/fc1fP93B22vcxx7/8SQyktr/NmF7NZ96jNDbhSJ9mkKWiAhQbasnq6SW9IHhZCZHctvckYQG+PGPyyYCkF1Syz+/3AVAbFgAM4fFHOlxXfLcTzM5KyORYItvj32HiPQ8rckSEQEKKqwAXDcrDR8fgx9lDubiyUkYhsEDH20lv+H83PQEnrpyco/WcsroeE4Zra1iRPo6jWSJiAAHKt0hKj480HPMMAwA3pg/nUER7gXpPTFFKCL9k0KWiAhNI1kJAwIPOTckOpibTh0O0KJxqIjIkWi6UEQEKGgYyUoIPzRkAVw0OYnjUqMZEh3c6nkRkYNpJEtEjnkul8nSHUWEB/oRdJjF5oZhKGCJSId0OmQZhpFgGMazhmH8cJjzJxmGsc4wjK8a/nNr58sUkb7gsmdWcM97m7xdRrvY6p1sL6gCYNmuYr7fW8plxw3xclUi0p90ZbpwJvA+MOEI19xkmuZXXfgOEekDau31XPL0Cjbur2D5nhLOnziQycm9d+3Skk35/OLVtThdJktumsXmvAoAbjhpmJcrE5H+pNMjWaZpvgVUtXHZlYZh/NYwjD8YhjG4s98lIr3bRxsL2LjfHVTiwgK48bV1PPP1bmz1Ti9XdiiH08X1L6/B6TIBd1f37QVVDIoIYoCaf4pIN+rJhe9bgD+appllGEY68KlhGGNM03QdfKFhGPOB+QBDhmi4XqQvqXe6eHF5FvHhAbxzw/Fkl9Rw97ubeOCjbeSVW1lwbrq3S2zh6x1FAEwaEkHigCCWbCogyN+XKXprUES6WY8tfDdNs9A0zayGz5uBCKDV0SzTNJ8xTTPTNM3M2NjYnipJRLpZYaWV619ew4bcCn535mgGRQQxY2gMX/z2JM4dP5D31+3HNE1vl+nxyspsrnlhFQnhgbw+fzq3zhlJvcukylbPqWr+KSLdrFtDlmEYIYZhxDZ8vsMwjKiGz1GABTjQnd8nIkff93tLySquobzWzsVPL+frHUXce/YYzpvQcrPkiUMiKKt1UFxt91KlLVkdThZ+tA2AW04fgcXPh5SYEP7544lcPDmJH2UmeblCEelvOj1daBjGicCVQKJhGHcDfwGuAjKA64Es4DHDMLYAY4ArTdO0drVgEfEe0zSZ/9IqIoL8mTk8hn2ltfz3/6aTmXLoVNvI+DAAthdUERsWcLRLbcFW7+T9dfupstXz6nXHMWNo076DZ48byNnjBnqxOhHprzodskzTXAosPejw483Ovw683tnni0jvk1tWR3mtg/JaB1klOcyblNRqwAIYkeAOWdsKKpk5vOc2Uz6Soiobzy7bw7+/zcLudDEsLpTpadFeqUVEjj3q+C4i7db4BuHxw6IprrJz91mjD3ttTGgAg6OC+CGrlGtnpbU4tyqrlP3ldYdMMXZFYZWV615cTUWtnXFJETxwYQbznvyOnNJaTh4Vx6CIIOakJ3j2IxQR6WkKWSLHsG93FfPS8myunZV62BGpRiXVNn7z3/X4+Rg899MpBPj5tBlYpqVG8+nWA2zNr+ShJds4fmgM152QxkVPLQfgjLGJWPy6Z2noHxZtYf2+cgCySmpxukxySmt59drjmDHMOyNpInJsU8gSOYY9/PF21u8rp87h5IWrp7Z6jdNl8vaaXAorrdQ5nDx80TgC/VvfeuZgp4yO483VuZzx2DIAvtpe5BkNAzj3n9/wi9nDOGd8x9ZEmabJnz/ezssrsokJDSAsyJ/1+8q5dmYqN502gmkPfM6HG/OZOSxGAUtEvEYhS+QYdec7GzwjP8t2FlFSbSM6tOUC9Vp7Pfe9v5k3V+cCkBwdzMWT2/8W3tyxiSw4ZwyvrMzht3NG8tLybP63Ps9zfltBFb96bS1rcso4cUQsU1KiCAlo+19Lu4uqeeKr3QBUWusJ8PPhpJGxXD0zldAAP566YjJPfLWLhy8a1+5aRUS6m0KWyDGmzu7kP99l8dr3+wB4aF4Gt7+9kWeW7cHmcDH/hDSW7Szin1/uYl9pXYt7r52V1uE1TVcdn8pVx6cCcPqYeBZvKuCW/67jicsnMWFwJH/7bAf//jaLf3+bRUp0MIt+NZMvthXy/d5SrpuVRlSohVdW5BAbFsApo+KIDLHw1XZ3Q9ElN80iKTIYPx+jxejazOExXltsLyLSyOhNjQIBMjMzzVWrVnm7DJF+69JnlrNiTykAn91yIqkxIUz8wydUWusPufaKaUNIiwnlgomDWLm3lDnp8d2ycNw0Tc9zTNPkqaV7WJtTxqdbDxDk70utvfXteEIsvlw+LZkP1ucRFujPxzef0OVaRES6wjCM1aZpZrZ2TiNZIv2cy2Vyz/ubmDc5iYTwQFbsKWVUQhiXHzeEYXGhAMybnMS/v8065N77z8/wfJ47NqHbamoe1AzD4OcnDQVg8cZ8PtiYz8j4ME4eFccL32WxfE8JCy8cx97iau55fzPPf7OXpMgg/vKj8d1Wj4hIT9BIlkg/t25fOec//m2LY+/cMINJQyI9P5umyfYDVQyPC8PXx+DZZXsYMzC8RdPO3qC42kZUsAUfH7VhEJHeQSNZIsewL7YVej5PT4vGxwfGDRrQ4hrDMBiVEO75+eC+Vr1FTKh3O8eLiHSEQpaIF20vqMLHgOENW9B0N1u9kzdX7WNaWhRPXD6ZqBBLj3yPiIgcSiFLxEue+GoXDy/Zjo8BP52RQnmtgyumJTM5ObLtm9tQZ3fi72vw3Dd7ya+wsnDeOAUsEZGjTCFLxAsqah3884tdHJcaRa3d6Vl0nlNay1vXT+/SG3zLdhYx/8XVGAZYHU7OzEjgBLUzEBE56hSyRNqhzu7Ex8fd/TzY0rn/2TS+ZJJVUsvsR74C3CNYJ46IZcmmAvLK6/jLpzu4+Y11/PWSCeSW1TE4KrjD3/Pumv3UOZxYfH04MyORhy8ap/36RES8QCFL5AhM0+SDDfnc+/4mymodDIkK5uvbZnfqWb97dxOvfZ/T4tjUVHeH83mTk3C6TKrt9Ty9dA/vrXN3RX/qikmcNDKuXfsENtqUV8Epo+J49qeZClciIl6kkCVyBO+t28/Nb6z3/JxTWsvijfkMigziiS93syanjJSYEP552UTK6xyMOGgBe42tnrdW5/LBhjx+yCoDIHFAIKeNiWduekKLt+V8fQxunzOK7/eWsjbHvd3N9S+vASA1JoS//Gh8i7YLAA6ni4ue/I4Af1/mpCcQ6O/DjgPVnDE2UQFLRMTL1CdL5CDPLtvDm6ty+cXJw3jwo60EWXx5/xfH89r3OTzw0bYj3js3PYG/XjKBIIsvLpfJqY8uZU9xjef8N7fPJinyyFOAlVYHW/MqWbqjiCe+2s38E9JYvCmffaV1HD8smt+fm86wOHeY+2hjPje8sqbF/SEWX165bhoTBkd08jcgIiLtdaQ+WQpZIs2szi5j3pPftTj23i+OZ8LgCBxOF+f84xsGRgSxt7gGHwPe/+VM7n53I++ty+Pc8QP53/o8xicN4IkrJpNfXsdFTy1nVEIYV05PZkpK1CEjXUfidJlYHU5CAvzILatl3pPfcaDSxtTUKN6YP42yWgdn/X0ZdQ4nN586guToYDbkVvB/J6YR4Ofb9heIiEiXKWSJtENBhZUfP7uCyjoHr8+fzk1vrOVnM1KZNznpiPeZpklJjZ2Y0ABu+e863lmzn3PHD6S0xs6anDK+v+tUQgO6PjNfVGXj2W/28PTSPTx/VSYbcyv562c7WPTLmWQkDWj7ASIi0u3U8V2OaVVWB2GB/oc9b6t3cttbG3h/XR6hAX78+2dTGBYXyge/mtWu5xuG4Vlb9eiPJhDg58Nr3+8D4P7zx3ZLwAKIDQvgltNG8NHGfK7+j/v/iJwwIlYBS0Skl1LIkn6pqMrGsp1FPLtsL1vyKxkQ5M+YxHAevDADXx+DKms9YwaG8/3eUn7y/EqsDhcXTU7i6uNTGTMwvO0vOIJfnzKChPAgpqREMmNY9/anCvDz5fX507n0meXsK63jtjkju/X5IiLSfTRdKH3Wpv0VuEyTcUktF3iX1tg55x/fsL+87pB7xicNYFNeJU6XyUkjY1m2sxiny2TsoHAW/XJmn3kjr9LqYF9pLekDNYolIuJNmi6UPm1XYRVDokKw+PkAsK+0lkUb8nh4yXYAshaexSebCzhQZePiyUnc/8EW9pfXccHEQfx2zkiWbCrg2WV7+OXJw7jr3U2e5+48UM1po+O5cnoyw+ND+0zAAggP9FfAEhHp5RSypFfbtL+Cs//xDVNTonjj/6bx7a4SrnhuZYtrVmeXMf+l1QD8+9u97Cmq8bRSALhmZirXzEzFNE3q7E7iwgM5d/zAo/5nERGRY4tClvRqjXv6fZ9VytIdRazcWwrAj48bQlign/tNu2/2YhiQHBXMnqIakiKDuO/cMYc8yzAMrp2VdjTLFxGRY5iPtwsQaY3TZXLzG+t4e00uV81IIXFAIE98uZu1OWWMSxrAAxdkcOLwWAA+3JjPKaPi+ddPMjllVBz//b/pJA4I8vKfQEREjnUayZJe6YMNeby7dj9XzUjhnrPHkBwdzO8XbQHgZ8enADAsPtRz/XWzUhkeH8ZzV03xRrkiHVdbCnVlED3U25WISA9RyJJex+kyeezznYyMD+Pes8fg42Nw6ZQhnpDVuJ4qLiyQN6+fTl55HcelRXuzZJGOqcyDf04FexX4WuCXP0BkirerEpFuppAlvYJpmry8Ipst+ZUs311CVkktT14+CR8f9xt/QRZfHv3ReL7cXtRiT74pKVHeKlmk8za+5Q5YAE477PwUpl7n3ZpEpNspZEmv8OLybO7732Yig/0pq3Vw4ohY5o5NaHHNhZOSuHDSkbe4EekTNr8DAyfBT96Dx8bD/jVt3yMifY5Clnid1eHkaVuR8wAAIABJREFUH1/sYnpaNK9edxxZJbUkhAf2qb5VIu1Wugfy1sLp90PgAEiaCvvVgFmkP1LIkqOuos7BtvxKvthWyMQhEdzz/maKq2389ZLxGIZBakyIt0sU6Rnl++B/N4LhA2POdx8bejIsuR0eGQFJU9znLnoefA+/36aI9A0KWXLU7CutJT48kFMfXUpRle2Q8zOGdu8+fyK9yuoXYNH/t3fe4VFUXQP/TRJCSCCQkBA6hN47SC8WuoBiQxQRe8Pyqa8oltfeK/YuFlDUFxFUUHpTeu+EXkIKBBLS7/fHmWV3w6ZnkwXP73n22Zk7M3fu3Z1y7rmnjJflER9AlTqy3OpyEbJOHYWtv0pZ/C6o1qxs2qkoSomhQpbidfbGJ/P8rC38semoW3loUAAPXNKEhJQMGlWriL+fTg8qZUR6Mky5FsoFw6jvSrbu5HhY8CL885GsD3ge2o1ybq9YDW6eC7vmwt7FsHs+JO5RIUtRzgNUyFK8zgcLdvPHpqPUqxpM5/rhDGxZnUpBATSIrEhkpfJl3TxFga2zRLgByM4CP/+SqffIBvhsIKSfgrrdYPArUL312fvV7iifU2Ph1UYiZCmKcs6jQpbiNdYfOE5qRjYz1x/i0rY1eWdU+7JukqJ45sA/zuWkg1ClbvHrzM6G6XdDQBAMewdaDM9feAuJEG3a8b3FP7+iKGWOClmKV0hMTmfYpCUABJXz4/qu9cq4RYqSg/QUCAwGY2DvUmd54p7iC1nZWfDX03B4LVz+sdhdFQTLkqCkCTEFP5cxkJUBAYFFaqqiKN5DcxcqXmHetlhA0t3Mub8PXaI1aKjiQ2z7HV6qD0vfgUWvwdGN0OM+2bZrXvHqzs6Gn26FJW9Cu9HQ+srCHV+1EcRtz1Fnlngl7l/hXp6ZBl9eCh/3k30Uxdc5vk+u2ZNH89/3PECFLKXESExOJyU9k/TMbH5ec5Co0PJMGNScOuHBZd00RXGSmQ6/3gdZaTB7Isx9BlpdARdOlO2LX4ejm4te/8pPYeM0qW/4u6KdKgzVmkNiDGSkOsv2LoHVX8KP49z3/fsD2LNIhMTXm8Psx+HkEdFsKYovsPMvOLBKBgFLJ8GbrSFmIWydUdYtKxVUyFJKBGMMI99fSosn/qDJxN9YtCOOMd3qn0mLoyg+welEmPMEnDwMl77lLB/+rsSlGjVF1mc+AIfXux8btwM+HwwnDjrLTsXClNHiGZgQI1N3yyZB3e7Q68HCC1gAkc3AZDu1WdnZ8PeHspwcD1mZ0o8po6UvDfpBZHMJAbH0bXitKbzfQxJQK0pZsXcpfD4Evr5cPHcProLZjzm3u95H5zFqk6UUi1kbDvP0jM2M7FiL3XHJANzUM5rgQH/Gdq9fto1TFGOcgo4xMOU6CZNQryd0uAGiWkH5SlAuSPZpOgjqXAD7lsFHfeCe1bDuO6jWQvIL7l0Cb7SA3g9B++vg0/7u8a2Gvys2Xd3uLpqABXIukCjwNdrAzjlSf1Qr0Vg9U1XauP9v2a/HvVCjLaz8DBa+ClEt5di/P4R+E4r80ylKkTEGpt8l2Q0ATh0RT1sQDe+8F+DACkknVatD2bWzFLCMMWXdBjc6depkVq78d6SYyMjKZtOhJBpXq0hI+dKTdzOysklJy6JycPEiSqdnZtP1hb9ISE4HIMDP4u9HL6JqRQ3LoPgAiXskfEKDvtBxLCTHwdTRksbmyi+gci3PxyXESDiHX+8Dyx+MbetUpZ6711+9HiJ0NR0igtp6WwvmVw7Gr3EGGy0sxsD73cEvAG5fBHOfE7ux/8TAizkM8htdAtd+D372pETGaShXAb4eCbFb4IFiTHsqSlGJWQRfDoXBr4rH7A9jReO6ZxE8dhR+uQfWfSv73vUPRDYt0+YWF8uyVhljOnnappqsMmD9geNEVCzPHV+vYt2BEzSMDGH63T2pWEqC1gPfr2PGukPsfn5wsabzNhw8TkJyOq9f1ZYdsae4uHk1FbAU3yAzHWbcJ9OC676TD4jn3tiZeXvihUfLZ/N02D0PanUSzdDxveJ12HyYTAk6BKwrv5D6HELWtVOKLmCBaMBaXQ5zn4W0U3B4HUQ0kTyH96yW0f/236DdtdDoYvdjy1WQ7wb9YOefMmUYrE4nSimSlSm2ggEV5Bo9JU5Q7J4HQVXAPwD6PyODl/VTJY/nOS5k5YUKWYUkIyub5LRMdsaeolP9wj+8YpNSGfHuEizLItsYRnaozY+rD7B8VzwXt4jyQoud7ItPoVZYBWasOyRtOZlG9cpBRa5v+W6x+ejbtBqXd6hdIm1UlGJjDPx0izzUh70jOQI3/AAn9kOTQQUPdXDNt7DrL8kt+F43EbJu+BXC6knam71Lodf/Oesb/aNMj+QUfIpCqK1lSzokL6GG/WS9akP5tMnHY7FqQ/lO2K1CllK6zH9BprebXwqBITJgGfIazPw/0SqDaLeGvycDmSMboO01Zdlir6JCViFISs1g1EfL2XQoCYCBLavTunZl7urXqMB1xMQlk22gcWQIl7SI4qae0fy4+gB74pPZcOAEzWpUYvnueKJCg2gSVanE2r4z9iQXv77QrWxvfHKRhayDx0/z6eIY2tSuTHiIxudRypDsbMhKF6PvdVOgfk/Y/D+4+CnoMEb26XxT4esNDJYXBcANv0DiXhGwQFLvmGz34KKNS0C4clDRHnAtfQuSY0V7VhjCG8h3wm6onWMWIyUB5jwu2q7WVxSu3uR4CCgP5Ss644wpiisHV8n3gOedZZ1vlmvY3+Vd4R8g9oeH1pZOu1ISxKGlaiOnJ3EpoEJWIXj2181nBKygcn78vukIv286woj2tahVpUKB6jiQeBqAj8Z0IjoiBGMMIYH+PD9rC9ku5nG1qlRgwUN9MUByWiZVgosmyGRmZTN3ayzPzDzbNmPprng61w/nk8W7eeevnVzeoRZPDWtJZrZhyc44ejaKIMDf6YC6YPsxokLLs/tYMl8s3UNKeiZvXt2uSO1SlBLh4CqYfg8c2yJCD0DCLvHu6z6+5M4TVl8+DixL7LW8hUPIWvutvIiaDSnc8WH1AQv+ekamGy+4XaYSQyLEvmvN17B5hkSh9y+AbaYxolH79BKxaWt5Gfz+CAx8AS64rbC9U85n4rZDm2vODuhbsdrZ+9bvCcvfgxn3Qq2OzkGRN4hZAJt+luX210v7iuqcUghUyCogSakZTFt1gLHd6/PUsJYAbDhwgksnLWblngRqtTvbiDY1I4s1+46zam8CN/VsQIVAf/YnpmBZULOKaJAsy6JOeDBbj5wEINDfj0taRjFz/WE+WxLDn5tj2XIkiSs61qZdnSoM93AeTxhjSDqdybgvV7BqbyJ1wivw0sjWNK0eSp2wCnR89k/e+msHU1fs50hSKv5+Fl8u20tiSgbHTqaxbHc8ACGB/ky9rRsB/hY3fPaP2zkmDGpGg8iKRf5NFaXIGCMxrv7+QASS1ldJ3Kv+z8r2SjWdxuDnIpWqy7fJhjZXFf5lEFBeRuzxO8R+bNkkO/n1FNi3XPZJOyFCXMcb8q5r40/w51NOo/99S+UDUt5hjNMWTPl3k5okaakimxRs/2ZDRQO96guZgvemkBW307n8Vhs59zXf5H9cRqporAsyGPGAClkFwBjDF0v2kG2gf0un3VTzGpUIDvTn3ilr+WPTEd69tgOW/TBMSs1g2DuL2ROfAsDO2FO8OLINK/YkEFUpiPIBzlGwY7ptXI9o7rmwEZUrlON0ehbPz9p6Zp8vlu4hNKgcQ9vUxN/Pwhhz5lw5OXIilUsnLebYyTQAXri8NSM71CYwwPnS6d0kksTkdDKysvH3s5hya1de/H0rv6w7RKUg52WRnJ7FyPeXkpaZfaYsLLgcX47rQpvaVYr8myoK2Vlg+YkAcfKoREhvMhAa9HFu9/OXh5x/OfepuQMrRHBoNhSGT4IKYWXTB29RIVy8C7MzoWUBU/LkZPgk+GyAc71iNYmtlX4Sut8j0ePnvyAvNsezJCtTpnEcHNsG0250rl84UQzyAQa9Ar89JMEmmw91P/fJIyL8loKmQCkFYrfIFHSAi2PT7vkQ3tDdySPeFmQiCihk1e4MlzwN63+Q2G/eJH6n2Dq2vEyeHVt/FVOD3AZjh9ZKRogtM6DZYHFwKQIqZBWA+duO8focCQzYvo7zYR7g78fzl7XmvqlrmbXhCNETZtEkqiK9G0fy55aj7IlPoXeTSCJCAvlpzUGmrzuEMSLguPJ//Zty4b5ExnSrf0YQuvvCRszdKl4ZO58bxMwNh7l3ylpe+WMbq/cmciAxhfeu60i7OmcLOn9uOXpGwBrYsjqjupydh+2rcV3OKnv32g68dbUIXSnpWSQkp3PidAY/rT7IZ0skl9q6J/oTFOjnJiQqSqFJT4a3O4hNT7UWcGClxNL5+wPoeqeo9o9tlxGxI75OSDUJxdDoYlj0qngvjXgfgkLLtCtewc8PKlaH0JpOO7DCUrcrPLIfXqwD1VrCyE/g51tFcGo8QIKeTr8Ljm6C6q1EMHqnI7QaCcPeljocXpnX/iCR6CvXlrARB1eJcLboNQkwuXWmCG5RLcSr8euREtdrxPtivF+3K1TQQdk5yZGN8EEPsasa8pqUZWXCV8NlObKZCFUjPxXnEpBwJwXBz0/ivMVth51zS7bd+/4WbXfSQfFmXD8F6nQVbXdAkDxDju8R4fHwOgiJlPsNxKv3mysgJV60yZt+hp4PSNy6QqJCVj5kZGXzyWIJqPbsiFZUCHQXLka0r0VQOT9u/3o1ANuPnmL70VOACDK9m0SSmpFF7bAKnEzLpHP98LOErI71wuhYz30k3r5OFfwsuKh5FAH+fnRvGIGfBR8s2HVmnzf/3M4XN54tLC3cfoxaVSow/e4ehAYVTsXpsMEKKR9ASPkA6gCtalWmc/0wypfzK3ZsLUUBYNtvIlRhiXF2eEMY94doSZZNEgGjTheZoopoChGNJejnwpflA3DRE+engOVgxHue7VgKQ1CohKyIbCb2WLcvdgZoPWEbx2//Hf6YIHZc6ackfU+/x8QzbNtvEN0HmvR31lmznXwABr0oMZAS94gw3GksrPlGBOKkg/DDDfL/NrwQrv+5eH1RSp/sLPjjUVle+TkMfFG0yq7x4jJSYMsvEijXEcW9ciG9zSuElbwma/ZEmS4/nQjT7HRUDfvJtd90sAhZuxdICq2poyGwktgXntgv7Uk+Bjf/JdPub7aW587VXxe6GSpk5UFmVjbjvljBkp3xjOlWj+u6epbOG1VzegH+fl8vlu2Kp0pwuTPCVFA5fx7oX7g4IJZlsfG/AyhnCz2Rlcoz7Y7uHDp+mqjQIL5ZvpfFO+MwxpCWmc1bf+1gwbZjdG9YlYU7jnFlxzpElGDMqkGta5RYXcp5iqcphdzYPB0q1YD7N8OJfaLG9y8HY3+FtJMy0sxpA5GVKdHPLX8RCApq93Gu4pg2LS71e7qvO6bwKtcSDcTcZ2Q9xsX7eMcfEjASJMl1brS8DCrXkejyG74XN30Qz86kQ/DPR7K+a65oK3N6Oiq+zYZpolWu0xX2L5fkzlUbSoopgJvmQHBVeKeDCFlJB0XDXNjp+wphkHnaGUy3JIjfIeFbEnbJtT30TdGEg2RFqFJPAg6DPG9ABC8HzYc5r9dON0rexSLEnVMhKw8WbD/Goh1xPDSgKbf2bpDrfvWqihtzRMVAmlUPpVn1khldBwe6/z0d6obRoa5cvJsOnuB/aw9x5zeriT2Zxqq9MgrYfFi8H0e0L5iBvKIUmsx0eWlWayYG5gGBdp6yQRAWDXcud6apycnu+ZL+ZcsvkpbGz8/daw8keron/AMk7Y1SckT3ceZIBKjZXjRSDgEL8vdsrN1JBOIN3zvLOtwgL9yYhTLNeGitTL/cvrjwWg6l7Fj7tdyf/SbI9ODm6TLV79AgV20kAUbLhUiuz8PrRHgvrC1eBVtwOZ3oLmTF7xIhvqCx7RykJEhdVRtK1PnD6+TadrSrXBDcuQxWfyXn6DFeBK1XG8v2ttfKQMFBs6Gw5C157hUy7IkKWblgjOHH1QcIDQrgll4NzmiUPFHO34/JN3UpVU87RyDU3zYeIcDP4unhLalVpQKT5u2kU70wOtRV+wfFS8y8X0IAgEQ8H/wKzHpI1hNjxO4n7aRonAa+CBGN5IG3+A15UDlo0K/026640/oKWP89XPS4TMe2u1ZePAdXi9dhy8sL9sKMbO5c/r/tMtoPDoe77PyKRzdJqqDtfxQtZplSehxYBTtmi43dnsXQ834ItQXjv/4r3xWj5OPQ6lRvBSs+luWqjQt/Tofma9dcMRU4vEZMCKbdKPG2ut2Vfx2H10FUaxm4xdtmNVUbyeCsdsez9w8Mga53uJfdvVI8e3MO9Gp1lDbumne2kJVPakIVsjyQmpHFt3/vY9aGI9zWp4GbV15u9Gocme8+JUmrWpVZ/J9+RFQsT2a2OZOS56Lm3o0ar/xLMQZWfCIPn3VTnOXbZsrHvzyMniYxbzZPF5uczFT47hoxVF/xsXjKNb8UBrwgL/Kmg8uuP4pQtys8stddkLr4qcLXExAINTtI7KFKHp5Bkc0lp6PDMFrxXabfJXHnKoSJ0XedCyA0h7nIqaPQyUVYbj7MmbA8p+BSEBzC2nQXYcrhobhvef5C1qG1ktDdYZx/dKOUF1bgi8hlfz9/55QpyPNw9ZcyyHTYiOaCClkeuOWrlSzaEUf9qsE8MrBZWTcnV2qHabRlpYQ4vl9iJkX3kujp9XtJeAWA2M2ihdrwg3P/MdNlqmnp22Kn0f0emVao2UGilHe5TaaP/nxKbCNCa8OA56DlCDn+wsdKu4dKbpRUmIVb5+W+zc9PXP2P7yuZcyneo3ItEbIcoTpqthetj4Mxv8CPN7vHV2t3rXjvDXzxbBvAghDkYebFMY2dGCOOFZXriLCz/Q+xA3O174vdIt8rPpEB3r5l8jxypJcqCepeIDlDTxwUb8PZj0kQ1XzweSHr942HaVWrcqkJFIt2iB0WSE6+3GJRKcp5xdTRom6fb69HNpcHm5+/eJwBYAG2arzOBfJy7pHjIRNSVeLegERdd/DAJq81XTlHqFJXUhM5vBsV38SROaF8JRGMc3q4NugDD+1wLwsOF3u7ohLVCga9DHOeEA24g4rVxUbwrbbQuL8Icd9eJduu/sYZny3ebk90H5hvp/Pp85+Svc4a9JXvSZ0hI9lZPvhV+O+tuR7m00JWbFLqmdAIW58ZSFC5wsdm2n3sFPO3HePGHvXzFZhiT6Zy/acS1fyBS5pwU8/owjdaUc41jm4SASuiqbhj1+oouf9A0k+kJYnXYJfbZIQbFl0wD6Ca7aXOnvd5t/3KuUFIpDg+zHkcLnlGBS1fJSVe4qhd861M8TsYPc0992BJ4ucn4RN2/iWerWHRMrXc6wH47WHZZ8dsCdAL4pk8dbSEJhk7UwKNVm0El38sglpoTUneXpLUbC+a+kOrnWXj19h5Qs9RISvuVPqZ5R1HT9G6dmWP+63Zl8hvG4/w8ICmZ+I8HT5xmu/+2c/bf4mE279lVJ7asB1HT/LJIgm4+fDAptzRp6FqsZTzn4xU+OMx8ay5cZao4bPS4dhWsZm6+En3/XPaZuRFQCDc/U/++yn/DpoOkinnpe+IAfHti1XQ8kVSEiR4rX+Ae/T/xpd4/9yOfIedxtl2WBYsf188GpNjYdssSaE18EWZrlv3nWi24nZCve5iD3j5h95r37C34YcbRZvXaqQzEXse+LSQlZDsFLK2HknyKGRlZxvGfbGCxJQMIiuW5xY71MIbc7bz/coDZ/b7c/NRxvaIZtXeBGKT0tziPhljGPXx38SdSuPi5lHc2beRF3ulFImTRyArQ7zWolqUdWvOPbKzxUBz++8Si+bW+aKN+vk22D0PetwnwSpB4lzduVxfgErJ4ngpfdRXDJMPripc3KwVn8KClyUpdasiphpS8iclvtCxoEqM3g9J8No2VznTaDk8VJ+NAox4p4ZUhcs+kPh6jthWjS/2fvuqt4Z7VhbqEN8WslKcQtb2oyfdtmVmZZOZbdgZe4rElAwAVu5N4BYa8PvGI3y/8gBtaldmaJsaPD9rK0/N2EyjapW47lP5w1ynH+dvO0bcqTQmDmnOzb3yl0yVUib1BLzmEsz1wZ1QsXS9Oc95lrwheeocbJ0p3jExC6H1lXBRDo2VCliKN6jZHh6Okft51ReFE7LWTJYsAX8+JUEmz+UE4L5KeoqYDARXLZvzV4qC639yL3MEN775L9Gs1Wjr3HbR4xIeZuWn0OKy0mtnISiykGVZVnXgWaCtMaazh+1+wPPAKaAe8KkxZnlB6p65/jAxcafOhCWoUTnoTKoaB3d9u5r5244xYZB4/9WqUuGMsDV5+R4qBQXwyZhOVAsNolqlIO6buvaMgAWwfHc8VUPKM/qT5SSlyrxzWw95AJUyJjMdvs+RmX3V59Dn4bJpT0mSlSHhDtJOiu1DSITEjirpHG8pCbDgFQmod9VX8EZL+NHF/fqiJ/WFpZQeweHiav/3B2I34xfgnmTYE0mH4NAaMZA+uhH+dzvU7SYRvHVAUHKcTpDvshKy8sJTrCsQw/O+E0S75YMUR5PVE5gOtMtl+1VAqDHmEcuywoHllmU1N8Zk5VfxXd+KYdk9FzbCsqBdnSpnIpk7+GPTUQCemrGZqiGBtKoVSkycWPwfOp5K7yaRVAuVqNMj2tfil3WHmLs1lu4Nq7J8dzzLdsUTE5d8RsACaFo9l0jTStmxZrIYyw59Q4SEqdfLvHyfh8VIEqDRRaXXHmMkunlgsIzK8yMr02nXEL8LkuPEFRgkhtTOP933bzwARn3nVJUXhYxUeSFt/VVyj5WvKCkr+j0q9V7/P5j3nLTlhhnudheKUhp0HCsx1b68VAyc798soQMcZKbLvd/wQgiPhmXvSnDbq76SjAHLJsH6qZJc3HE/KcUnQfL0uv0Xvo6fn0/PbBT56WqMmWZZVt88dhkCzLb3TbAsKxVoCazPq960zOwzy6v2JlKlQjkaRlZk9uajZGRlU87fj/TMbPwsyLa9yfs0jSTQ34/VKcd5YdYWYuKSuaSFe0C8x4e2YEjrGgxvV5PL3lvK3zEJ7Dp2iiZRFbmtd0MOJJ4udDJlxYsYA/OelwjhdbtBxxtlxNroIhEQXmkshpAAT50ovTZNGwebbHV2y8tgyOuihXJ1c87OlhgvS96SvF9XfCau6/Ofl7AITQZJ1OCdf0p8qahWEFhR4sxsmQGfDZCAnfuXS3qSwiZBXvTa2QHy6vWUfF0g6XCunlzkn0FRik3VxhIbyRGcNG67vNjTU2DtN5J2KWah5Je7db4YOLcYJnGPBjwHXW4Rt/69S1TIKkkO2PZGNTuUbTvOI7w5hK0GuBpSJdllZ2FZ1q3YPpCRtevj8AFcuisegPoRIWRlGz5etJuOdcOoWjGQbANvXN2WoW1qEuBn8dLv2zh2Mo0PF4okXrOye+606IgQoiMkoFrzGpXOGMW/Pao5/ZoWM9O9UjJkpsn8etwOOPCPCApNB8OwSc4pgVYjJVGnQ8ACmW7LLd9dcTEG9iySwJwbfhABq83VkqF908+w+RfxzOt0o7wIQmtJCIRVnzvr+GyAc7nDDTIC3/6brF9wuzOXW9PBkivsl3vgU9uIc/EbMoUYUF6mVjwF18sZd2jHbPm+Y5lMs8x/AS59s+R+E0UpLn5+Mnhy3Adx26FhP1jwojP1UvnKcv2+11WMsRu5eLeF1Rf3/b1LxM3fm5w8Ase2lVzCbl9k40+iIdyzSIJ5lpXh+3mIN4WsWMD1zRdql52FMeYj4COA2o1buSUCGt6uJtERIna9/Ps2AK7uJPP3jSIrnckpGB7iroWKCs0lQS3QJEqa9dxlrVTA8hUOr4NvrpR0DQ7q95JYLa4CRNWGMH61GGce3QzfXS3u4C2Geaddy9+DPx51Lxvwgsz/z3teov9u/VWmL6q3lngtR9bLC2DE+7DxRzhxQOytmgwUN+jON8Gn/UWL5Zos188POoyRZKkbp0ndB/6RtBKpx2HLrzD8HUlh4fhNUpPEW6vRxZLk9PcJcHgt9HtMvDCjWpSO142iFJYOY5xC1qovxW3/gIvnVs975V741Y6zFt3L/fh6PSTvouuUfOoJCPIc6oeTR0SAKKj94f4Vonn+6WZZfyLx/LVdXPiKZHYIrirxqpQSo0SFLMuyQoBgY8wxYCbQG5hs22QFAfmGfU7NzKJDVEV6NIpgUKsadK4fxukMdzOuqSv306txBC1rOqdRqgS7B0mrWzX3mFjXda1Hi5qhdGvgm4ZypcLKzyA4wnvCSWHIyoCfbhWbi8Gviu3SoTUSk8STUeuZUANBcswPN0hiz5JIoXBwlUxZRPeS/HqzJ8qIu3ob8Wyq3dlpYNnPFr6O/0e0R22vlQfV7vnywggOh1oe1O412sLDu91TVbjSfKh8HFq92p1lWmXKaHECuHCiuDpnZcKsByFhF/yzC/75UIyIW18J7UYX/7dQFG/SZCD0eUQ8w2I3SYiGo5vknjZZct1H95Z0KuknnTGUHNTrLsceWS/32fS7xY7rkqfPzkRweD182EumH2+YAWH18m5b0iGnNtlBcqwkDz6fWP89zHwQ0k6IVn3QS2XdovOO4ngX9gGuB2pYljUReA0YC7QGbge+B9pblvUkUBcYUxCj99PpWTSOqsSTl7Y8UxYcGIC/n0VWtmF4u5qs3pfIG1e3w8/P+QJ2tada/J9+eQYeDSrnT/eGEYXq73lFdhb8er8s1+wAzYaIVsXhKlvSHNkosUxqd4Eut0oE8WPbJMN7YAj8MUH2u/prSSBcUCpWg5tmw+TLYPbjMOrb3Ped/6IIb+HRIjA1G+IuwO2aC+umSr4947QLpEE/uOab3AUikId/Z3u0W7tTwdzS86rPQYUwqNPFeY5b5sJPt8C8F6BGO9HOoNRsAAAalElEQVSarZ8qL6qwepJ/sO3VMpWiKL6Onx/0myD3zrud5RmRehwGvSKDnGrNZb/cNLH1esj3rr9E4+zIrTnnCUnH0vZq5757l8r38b0yMBnxgWQ16DTO80DuqIs+oFYnOLgSkg6ef0LWsndFwAKJmK6UOMUxfF8ALMhR/K7L9mzgP4WtN9vAmK5njzJ+ubsHy3cncGP3+mQZc2aa0EGdcEnz8eyIVpo4OT8S98h3tZbiYTb3GdFsDZ8k3jwg2pzAYJmr3/SzPPB6PwT+RXAOmP+CTKlt+lm0Q0mHnDe2g6AqktG8sNTuJOrtha/AZwMlX9WBFeK95DBGj1noHiMK4NK3JcHpgZXy0D20RlJGtLlGXMTTT0l+vmGTfMf7zr+ctPvwOvjmCilr0A/6PqJu7Mq5S8VIuPgpZ7Ld+j2cAlZehNaQ4KbzX4JsCd/DlV+KkLX1VxGysrPEnvLQahG8Ot8kjjNvthKTg5rtxIYyJ8e2yve134tg9WFvmb73tO+5SuoJ0QI60MGZV/CRt4eTRtUqcoGHabyWNSvTsqbMtftx9gulZc3KrHjsYiIreUkbcz5xTGzbGPa2CCk/3iIanK9HSsykU7Gw/F2IaCLakuAI8fbJSpeHYW5kZ8PhNVAuRKa6Fr4iN/LBlRJRvEZb95FTi+FiCF6phmh2imrv0PkW8crbtwwmj5Cyec/JA7HvBPjrvzLlMHyS9H3LDJj5f2JTdeqI7F+9Ndz0J5TL3ZbPJwgKFY3W3Gdh7zKx+1IBSznXaT8Gjm2XKf+olvnv76BeD0hw8ZSt1VGeafvtdE7fjxGBC8SxpOudMujas0jK9iw5W3BKjhMzgeAIaDIAksUBi6RDReubr7L2W9HaXzgR1k2R6VmlxLGMMfnvVYp06tTJrFxZuLD1XsUY0Wp4y3utOCTslpxNNdoUTo298FXRXk044OxX2in4YrBoSVxpNlSEk1/Gi8r9we0Saylxr6j3e/2fcwTksIlwxS9A5vr7TpB4TSC/acZpSetSkgLCmm8kmWjTwSI0BgQ5M7pf+SW0tAWw04nw+6OAkfAJdbtKQlIfDWanKEoubPoZfhgry82GisnBskkiJN08Fz6xNfNNBkq+u/Bo8WL++wMZqNTtKs8GV2+62Y/D0rclRMuVX8jz6tko0STfOMs94nhurJsKGGh7zdnb5r0g2vHeDxWz88UgKwNebigBPq/7SQdqxcSyrFXGGI92Iv9eISsz7WwbJGNEA1KphszVZ2fCd6Ng5xzR8HjbVbgwJOyGd7tCVpqs95toe4UYmPOkjAi73+N+TOoJ8ZhZ/q54vN29wn17VqZodvzLSz2Je6GOPbrZME2ihHe4QbROO+ZA/A4RUu5YIvZO73QUL7cWwyT1RUo8TDwmiYJLC4enUdopMZ5dNxUiGot7uKIo5xeZafCsbRbgiJeXsBs+6if2XQDX/gBN+p997IKX5XkPkuonOFymF19vLoGGR01xCh+zHhbHkrBouHets45NP8M/H8PYme6CylO2h+NdKyCyibP8+H6ZqgR48rj7MaknRMNeIRwGPO9dM4W9S+HzQRLctcVw753nX0JeQpbPTRcCMu3kTVfZrbNgyigJCnnpW5IvCcSzbOErshxWD7bPFgELZMrJv9zZgktZseNPEbAGvgT7lsK8Z+XjStc7nZHDjZER3665sn7B7WfX6R/gHlLANcBm40vkAbP6S1kvFywhFvYsgteaizG7X4DEY6pcW0aVaUmlK2A5+gBOrdkFt5bu+RVFKT0CysNNc9wdScIbSAiT32xNUW4DrM43i/lC6nGxSQ0MgQUviaa75eXuAtDgl8VGdcnbIog5nqsOLdqJ/U7vxyxnFhG+vx5u/lOEwUWvS7YKB/uWQ71uzvW13zqN96s1k4G+t9gxR7w4o8/j2F8+gu8F/UhPgafDROPiLRxz9Nt/E7VwdpYEgPxqhHOfr0fKyKVcMFz/s5TNnginj7vXteRtiQI+/0XvtdfBzj/hx5vFzsAR+PKC20TdPdDD+V9tAsf3iWp40asiYLUYDiGREtSzMARVhntWQ5fbJMzChAMw7B3ZdvKQhFUY+6tTSAsOV0NKRVG8T50uZ9txtbSTBfd6MHdnneBw+M8escec+wz8/ogIWCChI3ISWku048nHnGXl7TBCh10MyE/sk+/WV0oIls8GwfS7ZAYhMQa6j5ftnw+ENV87jzuyQZ7NobUgxrYZi9sB73SSmYKSIjtb4vA16FPyeVKVs/A9TVaanaNwxcfOqaqS5thWGe1kpMr8/dGNsHsB1O8ptkNrv5EPSB65Bn0lNcq0cWIAHt1bLvranWHO4856u4+X0U5BOHkEZj0kdbcYkb89UOIeEfwAsAAjsZAco62ud8hn7zJJT7F0kmid3mwtAfiSY8UV+YrPi54Xz89PRnQOwqNh+HvyO4TVL32tlaIoiicqRsoUYIWwvPezLLjhV8mheGSDTBMmx4nnYk5Ca8p30iGnDWz5SvLO+uUeWPKmDMjj7fx/ncZJqJgfxsLRDVJWtZE4D1UIg8Vvwj8fQdtRkn7ryHoR+CqESRDjHvfKezB+h8QQG/h84X+HEwdEkOs+XtJ0rfjUqWToN7Hw9SmFxveErKx0+T7gJU1W2im5mbreITfUD2MleCTIhehwHz6+T5IQO0Y0zYeLrdYvLtOF4Q3ku6s9Sjm60RnXKNfznxR34OP7xe14yy9iG3D7IvfpOVeObJCbEWSOPyMFtv8u04E5caifB78sBprT7xTD+E7joOFFxUs87In2GvRSURQfpKCpYSpUgVvmiRarQphoqzxRyRa8Pu4H49fKwDLF9jysUlfMTZa+I4KN5Sfe2fW6i0NQ+im4fYkIWX7+Yt+bcVpmGBa97jT16HGvPLc3/ggf9ZGZFJDnf8sR+b9fXDFGMkskHRR7r5WfOR2BwKntU7yK7wlZ6clAgBgv7l0qHmg12+d9TM7cbXmx7TcR5JoMEoGk6RB41s7gXbOdfAeHy9SXK/4BMi3nmOcPiZQUMNVaiN3P8nfh0Nr8b4Kjm52ZzkdNFbuq78eIW3FrO/ZRxmkZ5bS5Sn6DGePF1bZOV6cRpaOtedF+tGjKzqWM6oqiKKWNf4Bov4BcX4sOTRbITEeXW0VoGfSKvAPe6y7BgRP3iLDkyExx4ywJblq9lXt9dbvKc901hl/bUTLIz86WdD5BlWHQyzD7MZhxH9y+uGD2ygdWSbDVpIOyvvw9MagfvwZit0jbdOahVPA9ISszFepcLKrNzwfJVNdDO3Lf/+QR8WqrUg/6PwONLnJu2/abCC/d7nLaCq35CkJrS6BJkAvt6q9h+x+5a5Ic1L0Ablt4drkxUDFKtG8OQ+vUJFEl5xT+4uwYVePXiCYsM00MENdMhj//KwLeYdt7ZYFtZ9XwQrGDqlwn7/Z5QgUsRVGU4hMS6Vxe+IrTScoxo1Gtudg6Wf7Q7W7nvjXaeg77EN0HLnxcAiGXDxWPcEcQ1lYjxag/urdo2gKCROjaOiN/b8CMVPjuGjERAZnC3DVPwkmE1nQXFhWv43tCVuU6cPmH8JZ9UaadzHv/XfNEFRu7SZLj3m0HoVv7LfzvDllOPQEj3pM8TTELof+z7qOB5pcWLp1LTixLguJtnSnxpBweeNG9JfTDotdlbr79aAmG6V9ehEKQGyk82jlleWKfJBfOTHVGZr9qstNbTlEURSl9/Pzhvo2QEidJ2R04NFQO4aVB3/wH7GDHynowl3P5ueeVbXW5eD7OfwmaXepZm5WSIF7wq76Q9ejeULe7DNIdmTyUUsf3hKyQCKhcVy7UuJ0ijXuaDjydCN9dK+ELHDiMHON3SZym6q3FKHv1ZNFkLXhJNFidbir5dkf3hk0/OQUsEIHuE1uztm2mqIVP7Ieo1u62UVUbSWT1NleLXVj1VqIuntRRbg4VsBRFUcqeKnXkc/9meKOFlFW0QwA1HSTe6hc+VvLn9fOXlGG5abNiFsE3V0LmaWfZ5R+ff7kWz0F8Oxjp0kkyF/2fvWe7mv4+QeaZQfLNZaWJgfg9q+DbayQ8ww2/ysX5+SDZr1YnmR/3RiLk9GRY8YlotKq3FsHwwAoxbG97Daz4TOKxhESIMFWvu/PYuJ2iiWs21F34ys4W4VKj8SqKovgWT1UGLHjKJayPIxiyN8jOgncvEG/DjmNh6Jvybji2Dd7vLsGzW18JvR8Wx63cEmsrJc65F4zUgWMOPDnubCFr1zxo3B+u+VaCYP72sDN+SfwOaD5MMrlnZTiP6XyzdwQskEB2Pe51L4vuJR/IO7FoRCP55MSbAVkVRVGUonPv+rNjcHkzSrufPwx4Dr69SqYEj24Sx7Cw+iJgjZsteRv9/N2jzCtlim8LWQ5vj+RYpxCy5huxX4rbDs0GOy/ykEixvco4Lelgmg2Rcv9ycM13cDpBNEiKoiiKUlzC6pX+OZsMkHQ8fz0Ni1+XspiFENFUHLMUn8O3hSyHJutUrATZXPSaM80NSN68nPv+85HEnwqLdm5rNtj7bVUURVEUb2NZcPGTYq879xnY/7eEg1B8Et8WsirVACz44Qb38rbXwobvRTXqINiOmD7nCfkOj0ZRFEVRzkuie8H1/5PA1K42vopP4dtGPyER0PN+97Lbl8Bl78Mj+5wJOeHsPHlRrb3ePEVRFEUpMwKDJbyDehH6LL4tZAG0v8653Li/MxGoa9Z1kNQxt8xzrueXC1BRFEVRFMWL+PZ0ITij6QKM/iHvfWt1kBx9jQd4t02KoiiKoij54PtClmXBkNck71JBGPqGd9ujKIqiKIpSAHxfyAKJb6UoiqIoinIO4fs2WYqiKIqiKOcgKmQpiqIoiqJ4ARWyFEVRFEVRvIAKWYqiKIqiKF5AhSxFURRFURQvoEKWoiiKoiiKF1AhS1EURVEUxQuokKUoiqIoiuIFVMhSFEVRFEXxAipkKYqiKIqieAEVshRFURRFUbyAClmKoiiKoiheQIUsRVEURVEUL2AZY8q6DW5YlnUS2FbW7ShDIoC4sm5EGaL91/7/W/v/b+47aP+1/+du/+sZYyI9bQgo7ZYUgG3GmE5l3YiywrKsldp/7X9Zt6Os+Df3/9/cd9D+a//Pz/7rdKGiKIqiKIoXUCFLURRFURTFC/iikPVRWTegjNH+/7vR/v97+Tf3HbT/2v/zEJ8zfFcURVEURTkf8EVNlqIoiqIoyjlPvt6FlmU1BJ4FVgO1gXhjzNOWZYUDLwK7gcbAo8aYo/YxHYHXgRXGmAdd6goF7gOSgI7AMmPMex7OeR3QHsgCdhljPrTLywPjgaeBSGPMqVzaXBl4FLjBGFPdpbwv8CZw3C6aaYx55Rzqf0GPbwS8CmQaY65wKQ8E/g9IBlrYfXnMx/qeW9v9gFuAZ4ALjTEbc2lznteIZVnNgBXAKGPMr7n13Qv9XwWcdKm+rjGmgYdzXgxcDsQCxhjz3xzbJwL3GWMiCtN/y7JaAC8Bi4BGwH5jzDPnSv8ty2oP3AtsBloCjxtj9nk4vh1wFxADVAMeNMZkWpY1FrgdSLV3/dQYM7kU+z8AuAFYD3QFHjbGbC9E/wt6/b8IBAOHgW52/7e71P0q8IkxZlJefffB/s8EQlx2bQPUNMak5jje4//vsn008DVQKbf3Rxn3v7p9zrbGmM45tl0FvADcm9uzy7KsN4AU4BTQFnlWHLGfC28B+4EawGJjzJRS7P/FwDXADqAZ8IAxJtHDOYv7/PN4/dv3z/P271IPuf+X59X/EsMYk+cH6AwMd1nfjLwkPwCusssuBSa77DMWeci/mqOuj4BoezkQaObhfLWBtTinMlcAje3lPkADwAAV82jzEKALEJejvC/QN78++3D/8z3e3jYauBWYlqP8caC3y3obX+p7Pm1vD7QD9gCt8mhzrtcIUAH4DFgKDC3l//5ql+V+wEQP5wsGdgLl7fUfgYtyXL+v5byuC9J/+/e71F72AxKAWudK/5F7or3LOad7ON4CNgLV7fXXgJtc2lU/v//ci/3f4tL++4GPC9n/gl7/T+B8dpw5DxAKXAF8Adx9Dvbf9fppAHxYmP/fXm8OPEc+74+y6r+97Qq7zpU5yqOR+2Y+eTy7gGddlv8DvGMv3we8ZS8HAHuBGqXRf8AfOAZUtdfvcbSloP+/vd6X/J9/uV3/1wDv2cvhwHbAvyD3QXE/+U4XGmNWGGOmuxT5IZqQIcAyu2yJve445gsg27Uey7Is4BLgQsuy7rcvgAMeTjkAWGXsX8M+xyC73gXGmN0FaPNMRBL2xPWWZT1oWdbTlmXVKUBdPtH/QhyPMeYbIN3DpmuBaMuy7rMs6xngSG79tusp7b7n2nZjzBpjzNq82mvvl9c18hyiCfD023iqq0T6b5dPdVm9DXlQ5aQbsNcYk5azbsuyopAHxTv5tNlj/+3fb4a9WgPRKp01ksxxjM/0HxkxOzRXu4GLPBzfAKhgjHFc125tA+627/0n7NF4npRk/4GjgCNYYSSwysM+ufa/ENf/0y7PjkbIixFjTJIxZlp+x+eoy5f673r9jMfzfZDr/29ZVjDwMPBfD8d5pAz6j/0fnfRQHmOMmVeANk/M0V6Htu7M/WNEsxcL9MinrpLqfzgQZIyJt9dzu39L4vnn8fp3bbMxJgHRaLfMq66SolA2WZZlXQb8YYzZiqhiHRdDEhBmWVZe04/VgPrADmPMG4g6z5PK2rVeR93VCtPOPNgMPGOMeRWYCsyx1YgFooz7X9Dj86I+ooJ9E1gAfF/QA0up717DsqwxwBJjTEwRjy9O/13raQCcMMZ4imzs8b93UXVPKErbc5z/LmAacI8xJqUQx5VZ/+3lxcg0C4iWOtjDOfM6fgHwkn3vrwR+KEh7Xdpd3P7fCzxhWdZrQHdgjod9SuTZZ1lWU8uyPgXq4FmYLTS+0n/b7KCu8TxdmtfxzyHP/gINsHJSSv0vMSzLqgL0BxzmMGfuH3tbE0S7WdD6itx/Y8wx4KBlWQ6hpksu5y6R518u17835Yo8KXDEd8uy+iHqyvvsoligEmLfFAokGpe5bw8k2d9/29+LgYmWZYUgakGQHyQWkUAdhCIqxLza9i7QEFhqjHk6t/2MMbEuy5vsi60OojrNEx/of4GON8b8L582uB7fy7Isf2NMVh7HlFrf82l7bm17Anlo7TLG3JXHrv2AbZZlPQLUBa6wLCvQGPNTAc5R3P674jYKtyxrGlAR+Bn5nyu57Btqn6sDkIFogMKACnY/fgRGUbD+A2CMedd+AK2xLCvGGLMhv2N8oP8A1wPjLbHZOwkcMmJrdebeB77J7fgcwvVc4JeCXPt2G4vVf0tsIWcCXY0xByzLuhT4DuhSiP7nVvdZ178xZhtwk2VZ44Avgavy62M+5/Cl/t+ETPk76s73/7dnLMKAq0SpDsADlmXNMsas9JX+G9v2tjBYlnUbcBlwytg2rJbYJL8HjLO1NgBTgFDLsh5F7p8VODXD+Z2jJO7/wcAtlmUdRt4H++y6S/z5l8v1H5tL3V6noKPPIUAvRBqvYVlWPeSi6YYY0vWw13PFGHPasqxliEp3C2J8tt0YkwwMdDlXbeAey7IsW+3XjfxVhPm+XOy6HwE+MsYk2NMFgYgaN7/jyrz/BT0+H/6yj99mH7+rAAJWqfW9KOQlVOfY70bHsmVZAxGbrzwN3+19i91/l7rOGoUbd+P+YKCeZVnlbZV5D8SOYCWifcGyrPqIncmL9mEF6r9lWVcg09AxxphUy7Jikf8hTyHLF/pvb67hmAqxLKs/MNk+/i6X4y3gtGVZ1e0pozNtsyzrBcRYPhOZOokpoIBVEv0vj0yZHLPXDwNBhey/R3Je/5ZlPWSczjwxyD1XZHyp/7ZGYwDivIR9fL7/vzFmP2Ir5NjvBeB1k4/he2n3vyjYgtkZ4cyyrAjk93nIGHPQsqyRxpgfEeHkN2PMPlvrdBWwML/6S/D+DzXGTLDrvBXn/Vuiz788rv+ZQG9gsv3uDwI2FaDdxcfkb/zXEZnXnW9/ViAXbDjwMTAR+ByIcjlmDDJaXAzc6lLeAvgEUftNxjbo9nDO65AL5TXgNpfy+vb5DPLj5mY83Q7xokm2969hnMZvk13OP+Qc639Bjx+OSPkbES8WR3ktl+O/Arr4YN9za3uYfb5E4G1kVOjp+DyvEeAB5OabDHQvrf7b2+4jH4N7xHbtQ8Sr58kc2xyel47rOqSg/QeGIqPnR4A3kIFLnoafvtR/+1yfAQ8idnUV8rj3P0UMYN8EAuzye+02P2r/Dh6vHy9e/7chRucTEK1Cz0L2v6DX//fI1NijyLRwD5dt4xFPsRm4GJKfC/23t40gH6P93P5/e1sk7vdGfo4fZdH/Pnb7D9r1V7DLLXvd8ewakMvxqxEPRkebZ9jlrZEpyomIl3HrUv7/30eeOQ/av4HHZ08+/39Bnn8er3/ENOol4En79833/i+pjwYjVRRFURRF8QIajFRRFEVRFMULqJClKIqiKIriBVTIUhRFURRF8QIqZCmKoiiKongBFbIURVEURVG8gApZiqKcN1iW1dOyrNWWJIPPbZ++liQSVhRF8SoqZCmKct5gjFmMxAnKi75IPCVFURSvUuC0OoqiKL6KZVlvA+WQ5LO17bJnkKwO6UiC2ocsy2qCCFnH7ejRLwKdgXFIbtOmwCPGc25FRVGUQqFClqIo5zR26o/GxphB9vpwe9NKY8x0u+wXy7JaGslZOh/YY4z5wk7FMgXJfnDQsqyxwGPA/aXeEUVRzjtUyFIU5VynJbDDZX23/R1oWdbLQAKSUirSw7ERSJqQ6+3kweFAvjkNFUVRCoIKWYqinOtsBi50WW8AVEFyvIUaY9Ity2rjsj0LySdcC5lKPAZ8aIxJtCyrKpL8VlEUpdho7kJFUc5p7Cm/SUB5YD/QH9iKaKXSgJVI0vVVwO1Ad2Q6MAV4CKiHJL7dD9QFXjfGbCnVTiiKcl6iQpaiKIqiKIoX0BAOiqIoiqIoXkCFLEVRFEVRFC+gQpaiKIqiKIoXUCFLURRFURTFC6iQpSiKoiiK4gVUyFIURVEURfECKmQpiqIoiqJ4ARWyFEVRFEVRvMD/AwU/ygLPuHIYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hs300[['strategy','cum_ret']].dropna().plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 改变算法：SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = hs_7d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>close - 1d</th>\n",
       "      <th>close - 2d</th>\n",
       "      <th>close - 3d</th>\n",
       "      <th>close - 4d</th>\n",
       "      <th>close - 5d</th>\n",
       "      <th>close - 6d</th>\n",
       "      <th>close - 7d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-01-14</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3478.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>3118.73</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3539.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>3130.73</td>\n",
       "      <td>3118.73</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3294.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-19</td>\n",
       "      <td>3223.12</td>\n",
       "      <td>3130.73</td>\n",
       "      <td>3118.73</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3361.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-20</td>\n",
       "      <td>3174.38</td>\n",
       "      <td>3223.12</td>\n",
       "      <td>3130.73</td>\n",
       "      <td>3118.73</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3192.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-12</td>\n",
       "      <td>3699.10</td>\n",
       "      <td>3633.53</td>\n",
       "      <td>3669.29</td>\n",
       "      <td>3621.43</td>\n",
       "      <td>3636.33</td>\n",
       "      <td>3675.69</td>\n",
       "      <td>3747.44</td>\n",
       "      <td>3803.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-13</td>\n",
       "      <td>3665.75</td>\n",
       "      <td>3699.10</td>\n",
       "      <td>3633.53</td>\n",
       "      <td>3669.29</td>\n",
       "      <td>3621.43</td>\n",
       "      <td>3636.33</td>\n",
       "      <td>3675.69</td>\n",
       "      <td>3747.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-14</td>\n",
       "      <td>3682.40</td>\n",
       "      <td>3665.75</td>\n",
       "      <td>3699.10</td>\n",
       "      <td>3633.53</td>\n",
       "      <td>3669.29</td>\n",
       "      <td>3621.43</td>\n",
       "      <td>3636.33</td>\n",
       "      <td>3675.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>3694.00</td>\n",
       "      <td>3682.40</td>\n",
       "      <td>3665.75</td>\n",
       "      <td>3699.10</td>\n",
       "      <td>3633.53</td>\n",
       "      <td>3669.29</td>\n",
       "      <td>3621.43</td>\n",
       "      <td>3636.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-16</td>\n",
       "      <td>3710.54</td>\n",
       "      <td>3694.00</td>\n",
       "      <td>3682.40</td>\n",
       "      <td>3665.75</td>\n",
       "      <td>3699.10</td>\n",
       "      <td>3633.53</td>\n",
       "      <td>3669.29</td>\n",
       "      <td>3621.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>876 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              close  close - 1d  close - 2d  close - 3d  close - 4d  \\\n",
       "date                                                                  \n",
       "2016-01-14  3221.57     3155.88     3215.71     3192.45     3361.56   \n",
       "2016-01-15  3118.73     3221.57     3155.88     3215.71     3192.45   \n",
       "2016-01-18  3130.73     3118.73     3221.57     3155.88     3215.71   \n",
       "2016-01-19  3223.12     3130.73     3118.73     3221.57     3155.88   \n",
       "2016-01-20  3174.38     3223.12     3130.73     3118.73     3221.57   \n",
       "...             ...         ...         ...         ...         ...   \n",
       "2019-08-12  3699.10     3633.53     3669.29     3621.43     3636.33   \n",
       "2019-08-13  3665.75     3699.10     3633.53     3669.29     3621.43   \n",
       "2019-08-14  3682.40     3665.75     3699.10     3633.53     3669.29   \n",
       "2019-08-15  3694.00     3682.40     3665.75     3699.10     3633.53   \n",
       "2019-08-16  3710.54     3694.00     3682.40     3665.75     3699.10   \n",
       "\n",
       "            close - 5d  close - 6d  close - 7d  \n",
       "date                                            \n",
       "2016-01-14     3294.38     3539.81     3478.78  \n",
       "2016-01-15     3361.56     3294.38     3539.81  \n",
       "2016-01-18     3192.45     3361.56     3294.38  \n",
       "2016-01-19     3215.71     3192.45     3361.56  \n",
       "2016-01-20     3155.88     3215.71     3192.45  \n",
       "...                ...         ...         ...  \n",
       "2019-08-12     3675.69     3747.44     3803.47  \n",
       "2019-08-13     3636.33     3675.69     3747.44  \n",
       "2019-08-14     3621.43     3636.33     3675.69  \n",
       "2019-08-15     3669.29     3621.43     3636.33  \n",
       "2019-08-16     3633.53     3669.29     3621.43  \n",
       "\n",
       "[876 rows x 8 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf_SVC = SVC(kernel = 'linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "    kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_SVC.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.541095890410959"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_SVC.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300['prediction'] = np.NaN\n",
    "hs300['prediction'].ix[7:] = clf_SVC.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300['strategy'] = (hs300['prediction'].shift(1) * hs300['returns'] + 1).cumprod()\n",
    "hs300['cum_ret'] = (hs300['returns']+1).cumprod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1243a7310>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hs300[['strategy','cum_ret']].dropna().plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. 逻辑回归算法在测试集的验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2\n"
     ]
    }
   ],
   "source": [
    "hs300_test = ts.get_k_data('hs300', start = '2019-08-19', end = '2020-08-18')      #测试集数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300_test.set_index('date', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300_test['returns'] = hs300_test['close'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300_test.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>code</th>\n",
       "      <th>returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-08-20</td>\n",
       "      <td>3786.03</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>3799.90</td>\n",
       "      <td>3778.73</td>\n",
       "      <td>108602088.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.000886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-21</td>\n",
       "      <td>3778.31</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>3792.60</td>\n",
       "      <td>3775.45</td>\n",
       "      <td>82299880.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.001576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-22</td>\n",
       "      <td>3793.86</td>\n",
       "      <td>3793.51</td>\n",
       "      <td>3797.33</td>\n",
       "      <td>3771.20</td>\n",
       "      <td>85807792.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.003107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-23</td>\n",
       "      <td>3796.88</td>\n",
       "      <td>3820.86</td>\n",
       "      <td>3829.36</td>\n",
       "      <td>3788.03</td>\n",
       "      <td>87954583.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.007210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-26</td>\n",
       "      <td>3756.36</td>\n",
       "      <td>3765.91</td>\n",
       "      <td>3778.87</td>\n",
       "      <td>3753.16</td>\n",
       "      <td>93859269.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.014382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               open    close     high      low       volume   code   returns\n",
       "date                                                                        \n",
       "2019-08-20  3786.03  3787.73  3799.90  3778.73  108602088.0  hs300 -0.000886\n",
       "2019-08-21  3778.31  3781.76  3792.60  3775.45   82299880.0  hs300 -0.001576\n",
       "2019-08-22  3793.86  3793.51  3797.33  3771.20   85807792.0  hs300  0.003107\n",
       "2019-08-23  3796.88  3820.86  3829.36  3788.03   87954583.0  hs300  0.007210\n",
       "2019-08-26  3756.36  3765.91  3778.87  3753.16   93859269.0  hs300 -0.014382"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs300_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1, 8, 1):\n",
    "    hs300_test['close - ' + str(i) + 'd'] = hs300_test['close'].shift(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>code</th>\n",
       "      <th>returns</th>\n",
       "      <th>close - 1d</th>\n",
       "      <th>close - 2d</th>\n",
       "      <th>close - 3d</th>\n",
       "      <th>close - 4d</th>\n",
       "      <th>close - 5d</th>\n",
       "      <th>close - 6d</th>\n",
       "      <th>close - 7d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-08-20</td>\n",
       "      <td>3786.03</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>3799.90</td>\n",
       "      <td>3778.73</td>\n",
       "      <td>108602088.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.000886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-21</td>\n",
       "      <td>3778.31</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>3792.60</td>\n",
       "      <td>3775.45</td>\n",
       "      <td>82299880.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.001576</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-22</td>\n",
       "      <td>3793.86</td>\n",
       "      <td>3793.51</td>\n",
       "      <td>3797.33</td>\n",
       "      <td>3771.20</td>\n",
       "      <td>85807792.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.003107</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-23</td>\n",
       "      <td>3796.88</td>\n",
       "      <td>3820.86</td>\n",
       "      <td>3829.36</td>\n",
       "      <td>3788.03</td>\n",
       "      <td>87954583.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.007210</td>\n",
       "      <td>3793.51</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-26</td>\n",
       "      <td>3756.36</td>\n",
       "      <td>3765.91</td>\n",
       "      <td>3778.87</td>\n",
       "      <td>3753.16</td>\n",
       "      <td>93859269.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.014382</td>\n",
       "      <td>3820.86</td>\n",
       "      <td>3793.51</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               open    close     high      low       volume   code   returns  \\\n",
       "date                                                                           \n",
       "2019-08-20  3786.03  3787.73  3799.90  3778.73  108602088.0  hs300 -0.000886   \n",
       "2019-08-21  3778.31  3781.76  3792.60  3775.45   82299880.0  hs300 -0.001576   \n",
       "2019-08-22  3793.86  3793.51  3797.33  3771.20   85807792.0  hs300  0.003107   \n",
       "2019-08-23  3796.88  3820.86  3829.36  3788.03   87954583.0  hs300  0.007210   \n",
       "2019-08-26  3756.36  3765.91  3778.87  3753.16   93859269.0  hs300 -0.014382   \n",
       "\n",
       "            close - 1d  close - 2d  close - 3d  close - 4d  close - 5d  \\\n",
       "date                                                                     \n",
       "2019-08-20         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-08-21     3787.73         NaN         NaN         NaN         NaN   \n",
       "2019-08-22     3781.76     3787.73         NaN         NaN         NaN   \n",
       "2019-08-23     3793.51     3781.76     3787.73         NaN         NaN   \n",
       "2019-08-26     3820.86     3793.51     3781.76     3787.73         NaN   \n",
       "\n",
       "            close - 6d  close - 7d  \n",
       "date                                \n",
       "2019-08-20         NaN         NaN  \n",
       "2019-08-21         NaN         NaN  \n",
       "2019-08-22         NaN         NaN  \n",
       "2019-08-23         NaN         NaN  \n",
       "2019-08-26         NaN         NaN  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs300_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs_7d_test = hs300_test[[x for x in hs300_test.columns if 'close' in x]].iloc[7:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>close</th>\n",
       "      <th>close - 1d</th>\n",
       "      <th>close - 2d</th>\n",
       "      <th>close - 3d</th>\n",
       "      <th>close - 4d</th>\n",
       "      <th>close - 5d</th>\n",
       "      <th>close - 6d</th>\n",
       "      <th>close - 7d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>3790.19</td>\n",
       "      <td>3802.58</td>\n",
       "      <td>3816.95</td>\n",
       "      <td>3765.91</td>\n",
       "      <td>3820.86</td>\n",
       "      <td>3793.51</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>3787.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>3799.59</td>\n",
       "      <td>3790.19</td>\n",
       "      <td>3802.58</td>\n",
       "      <td>3816.95</td>\n",
       "      <td>3765.91</td>\n",
       "      <td>3820.86</td>\n",
       "      <td>3793.51</td>\n",
       "      <td>3781.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>3848.32</td>\n",
       "      <td>3799.59</td>\n",
       "      <td>3790.19</td>\n",
       "      <td>3802.58</td>\n",
       "      <td>3816.95</td>\n",
       "      <td>3765.91</td>\n",
       "      <td>3820.86</td>\n",
       "      <td>3793.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-03</td>\n",
       "      <td>3853.61</td>\n",
       "      <td>3848.32</td>\n",
       "      <td>3799.59</td>\n",
       "      <td>3790.19</td>\n",
       "      <td>3802.58</td>\n",
       "      <td>3816.95</td>\n",
       "      <td>3765.91</td>\n",
       "      <td>3820.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-04</td>\n",
       "      <td>3886.00</td>\n",
       "      <td>3853.61</td>\n",
       "      <td>3848.32</td>\n",
       "      <td>3799.59</td>\n",
       "      <td>3790.19</td>\n",
       "      <td>3802.58</td>\n",
       "      <td>3816.95</td>\n",
       "      <td>3765.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              close  close - 1d  close - 2d  close - 3d  close - 4d  \\\n",
       "date                                                                  \n",
       "2019-08-29  3790.19     3802.58     3816.95     3765.91     3820.86   \n",
       "2019-08-30  3799.59     3790.19     3802.58     3816.95     3765.91   \n",
       "2019-09-02  3848.32     3799.59     3790.19     3802.58     3816.95   \n",
       "2019-09-03  3853.61     3848.32     3799.59     3790.19     3802.58   \n",
       "2019-09-04  3886.00     3853.61     3848.32     3799.59     3790.19   \n",
       "\n",
       "            close - 5d  close - 6d  close - 7d  \n",
       "date                                            \n",
       "2019-08-29     3793.51     3781.76     3787.73  \n",
       "2019-08-30     3820.86     3793.51     3781.76  \n",
       "2019-09-02     3765.91     3820.86     3793.51  \n",
       "2019-09-03     3816.95     3765.91     3820.86  \n",
       "2019-09-04     3802.58     3816.95     3765.91  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs_7d_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.86497133, -0.81871021, -0.76311202, ..., -0.8301514 ,\n",
       "        -0.86929194, -0.8433097 ],\n",
       "       [-0.83246104, -0.86217511, -0.81406509, ..., -0.73039888,\n",
       "        -0.82588816, -0.86564315],\n",
       "       [-0.66392631, -0.82919932, -0.85799749, ..., -0.93081573,\n",
       "        -0.72485896, -0.82168703],\n",
       "       ...,\n",
       "       [ 2.05929469,  2.14581338,  2.30339771, ...,  2.70495382,\n",
       "         2.80746939,  2.85300715],\n",
       "       [ 2.29765737,  2.10396219,  2.18234427, ...,  2.50497465,\n",
       "         2.75446138,  2.85790779],\n",
       "       [ 2.68017206,  2.34573788,  2.14004294, ...,  2.5667592 ,\n",
       "         2.55192275,  2.80422521]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = hs_7d_test \n",
    "X_test = sklearn.preprocessing.scale(X_test)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300_test['prediction'] = np.NaN\n",
    "hs300_test['prediction'].ix[7:] = lm.predict(X_test)    #给你测试集的features，返回的是预测的测试集的labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1.0    136\n",
       "-1.0     96\n",
       " 0.0      2\n",
       "Name: prediction, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs300_test['prediction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>2019-08-20</td>\n",
       "      <td>3786.03</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>3799.90</td>\n",
       "      <td>3778.73</td>\n",
       "      <td>108602088.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.000886</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>2019-08-21</td>\n",
       "      <td>3778.31</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>3792.60</td>\n",
       "      <td>3775.45</td>\n",
       "      <td>82299880.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.001576</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-22</td>\n",
       "      <td>3793.86</td>\n",
       "      <td>3793.51</td>\n",
       "      <td>3797.33</td>\n",
       "      <td>3771.20</td>\n",
       "      <td>85807792.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.003107</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-23</td>\n",
       "      <td>3796.88</td>\n",
       "      <td>3820.86</td>\n",
       "      <td>3829.36</td>\n",
       "      <td>3788.03</td>\n",
       "      <td>87954583.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.007210</td>\n",
       "      <td>3793.51</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-26</td>\n",
       "      <td>3756.36</td>\n",
       "      <td>3765.91</td>\n",
       "      <td>3778.87</td>\n",
       "      <td>3753.16</td>\n",
       "      <td>93859269.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.014382</td>\n",
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       "      <td>3793.51</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-27</td>\n",
       "      <td>3791.46</td>\n",
       "      <td>3816.95</td>\n",
       "      <td>3848.24</td>\n",
       "      <td>3790.87</td>\n",
       "      <td>124731478.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.013553</td>\n",
       "      <td>3765.91</td>\n",
       "      <td>3820.86</td>\n",
       "      <td>3793.51</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-28</td>\n",
       "      <td>3818.81</td>\n",
       "      <td>3802.58</td>\n",
       "      <td>3822.59</td>\n",
       "      <td>3790.14</td>\n",
       "      <td>88750812.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.003765</td>\n",
       "      <td>3816.95</td>\n",
       "      <td>3765.91</td>\n",
       "      <td>3820.86</td>\n",
       "      <td>3793.51</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>3805.49</td>\n",
       "      <td>3790.19</td>\n",
       "      <td>3808.60</td>\n",
       "      <td>3774.40</td>\n",
       "      <td>84083387.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>-0.003258</td>\n",
       "      <td>3802.58</td>\n",
       "      <td>3816.95</td>\n",
       "      <td>3765.91</td>\n",
       "      <td>3820.86</td>\n",
       "      <td>3793.51</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>3787.73</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>3816.68</td>\n",
       "      <td>3799.59</td>\n",
       "      <td>3828.97</td>\n",
       "      <td>3778.92</td>\n",
       "      <td>96695150.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.002480</td>\n",
       "      <td>3790.19</td>\n",
       "      <td>3802.58</td>\n",
       "      <td>3816.95</td>\n",
       "      <td>3765.91</td>\n",
       "      <td>3820.86</td>\n",
       "      <td>3793.51</td>\n",
       "      <td>3781.76</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>3803.69</td>\n",
       "      <td>3848.32</td>\n",
       "      <td>3855.78</td>\n",
       "      <td>3799.82</td>\n",
       "      <td>110289349.0</td>\n",
       "      <td>hs300</td>\n",
       "      <td>0.012825</td>\n",
       "      <td>3799.59</td>\n",
       "      <td>3790.19</td>\n",
       "      <td>3802.58</td>\n",
       "      <td>3816.95</td>\n",
       "      <td>3765.91</td>\n",
       "      <td>3820.86</td>\n",
       "      <td>3793.51</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               open    close     high      low       volume   code   returns  \\\n",
       "date                                                                           \n",
       "2019-08-20  3786.03  3787.73  3799.90  3778.73  108602088.0  hs300 -0.000886   \n",
       "2019-08-21  3778.31  3781.76  3792.60  3775.45   82299880.0  hs300 -0.001576   \n",
       "2019-08-22  3793.86  3793.51  3797.33  3771.20   85807792.0  hs300  0.003107   \n",
       "2019-08-23  3796.88  3820.86  3829.36  3788.03   87954583.0  hs300  0.007210   \n",
       "2019-08-26  3756.36  3765.91  3778.87  3753.16   93859269.0  hs300 -0.014382   \n",
       "2019-08-27  3791.46  3816.95  3848.24  3790.87  124731478.0  hs300  0.013553   \n",
       "2019-08-28  3818.81  3802.58  3822.59  3790.14   88750812.0  hs300 -0.003765   \n",
       "2019-08-29  3805.49  3790.19  3808.60  3774.40   84083387.0  hs300 -0.003258   \n",
       "2019-08-30  3816.68  3799.59  3828.97  3778.92   96695150.0  hs300  0.002480   \n",
       "2019-09-02  3803.69  3848.32  3855.78  3799.82  110289349.0  hs300  0.012825   \n",
       "\n",
       "            close - 1d  close - 2d  close - 3d  close - 4d  close - 5d  \\\n",
       "date                                                                     \n",
       "2019-08-20         NaN         NaN         NaN         NaN         NaN   \n",
       "2019-08-21     3787.73         NaN         NaN         NaN         NaN   \n",
       "2019-08-22     3781.76     3787.73         NaN         NaN         NaN   \n",
       "2019-08-23     3793.51     3781.76     3787.73         NaN         NaN   \n",
       "2019-08-26     3820.86     3793.51     3781.76     3787.73         NaN   \n",
       "2019-08-27     3765.91     3820.86     3793.51     3781.76     3787.73   \n",
       "2019-08-28     3816.95     3765.91     3820.86     3793.51     3781.76   \n",
       "2019-08-29     3802.58     3816.95     3765.91     3820.86     3793.51   \n",
       "2019-08-30     3790.19     3802.58     3816.95     3765.91     3820.86   \n",
       "2019-09-02     3799.59     3790.19     3802.58     3816.95     3765.91   \n",
       "\n",
       "            close - 6d  close - 7d  prediction  \n",
       "date                                            \n",
       "2019-08-20         NaN         NaN         NaN  \n",
       "2019-08-21         NaN         NaN         NaN  \n",
       "2019-08-22         NaN         NaN         NaN  \n",
       "2019-08-23         NaN         NaN         NaN  \n",
       "2019-08-26         NaN         NaN         NaN  \n",
       "2019-08-27         NaN         NaN         NaN  \n",
       "2019-08-28     3787.73         NaN         NaN  \n",
       "2019-08-29     3781.76     3787.73         1.0  \n",
       "2019-08-30     3793.51     3781.76         1.0  \n",
       "2019-09-02     3820.86     3793.51        -1.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs300_test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300_test['strategy'] = (hs300_test['prediction'].shift(1) * hs300_test['returns'] + 1).cumprod()\n",
    "hs300_test['cum_ret'] = (hs300_test['returns']+1).cumprod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1243a97d0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hs300_test[['strategy','cum_ret']].dropna().plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.SVM算法在测试集的验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = hs_7d_test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300_test['prediction'] = np.NaN\n",
    "hs300_test['prediction'].ix[7:] = clf_SVC.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs300_test['strategy'] = (hs300_test['prediction'].shift(1) * hs300_test['returns'] + 1).cumprod()\n",
    "hs300_test['cum_ret'] = (hs300_test['returns']+1).cumprod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x124dcec50>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hs300_test[['strategy','cum_ret']].dropna().plot(figsize=(10, 6))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
